{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T14:48:28Z","timestamp":1781880508225,"version":"3.54.5"},"reference-count":259,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Republic of Cyprus","award":["CODEVELOP-ICTHEALTH\/0322\/0071"],"award-info":[{"award-number":["CODEVELOP-ICTHEALTH\/0322\/0071"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Artificial intelligence (AI) is increasingly applied in a wide range of healthcare and Intensive Care Unit (ICU) areas to serve\u2014among others\u2014as a tool for disease detection and prediction, as well as for healthcare resources\u2019 management. Since sepsis is a high mortality and rapidly developing organ dysfunction disease afflicting millions in ICUs and costing huge amounts to treat, the area can benefit from the use of AI tools for early and informed diagnosis and antibiotic administration. Additionally, resource allocation plays a crucial role when patient flow is increased, and resources are limited. At the same time, sensitive data use raises the need for ethical guidelines and reflective datasets. Additionally, explainable AI is applied to handle AI opaqueness. This study aims to present existing clinical approaches for infection assessment in terms of scoring systems and diagnostic biomarkers, along with their limitations, and an extensive overview of AI applications in healthcare and ICUs in terms of (a) sepsis detection\/prediction and sepsis mortality prediction, (b) length of ICU\/hospital stay prediction, and (c) ICU admission\/hospitalization prediction after Emergency Department admission, each constituting an important factor towards either prompt interventions and improved patient wellbeing or efficient resource management. Challenges of AI applications in ICU are addressed, along with useful recommendations to mitigate them. Explainable AI applications in ICU are described, and their value in validating, and translating predictions in the clinical setting is highlighted. The most important findings and future directions including multimodal data use and Transformer-based models are discussed. The goal is to make research in AI advances in ICU and particularly sepsis prediction more accessible and provide useful directions on future work.<\/jats:p>","DOI":"10.3390\/make7010006","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T09:30:45Z","timestamp":1736328645000},"page":"6","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3568-3449","authenticated-orcid":false,"given":"Charithea","family":"Stylianides","sequence":"first","affiliation":[{"name":"Centre of Excellence, CYENS, Nicosia 1016, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andria","family":"Nicolaou","sequence":"additional","affiliation":[{"name":"Centre of Excellence, CYENS, Nicosia 1016, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waqar Aziz","family":"Sulaiman","sequence":"additional","affiliation":[{"name":"Centre of Excellence, CYENS, Nicosia 1016, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina-Athanasia","family":"Alexandropoulou","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 176 76 Kallithea, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilias","family":"Panagiotopoulos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 176 76 Kallithea, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantina","family":"Karathanasopoulou","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 176 76 Kallithea, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Dimitrakopoulos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 176 76 Kallithea, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Styliani","family":"Kleanthous","sequence":"additional","affiliation":[{"name":"Centre of Excellence, CYENS, Nicosia 1016, Cyprus"},{"name":"Faculty of Pure and Applied Sciences, Open University of Cyprus, Latsia 2220, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8795-5560","authenticated-orcid":false,"given":"Eleni","family":"Politi","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 176 76 Kallithea, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris","family":"Ntalaperas","sequence":"additional","affiliation":[{"name":"UBITECH Limited, Limassol 3071, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2579-8648","authenticated-orcid":false,"given":"Xanthi","family":"Papageorgiou","sequence":"additional","affiliation":[{"name":"UBITECH Limited, Limassol 3071, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fransisco","family":"Garcia","sequence":"additional","affiliation":[{"name":"Research & Development Department, 3aHealth, Strovolos 2020, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5148-5197","authenticated-orcid":false,"given":"Zinonas","family":"Antoniou","sequence":"additional","affiliation":[{"name":"Research & Development Department, 3aHealth, Strovolos 2020, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikos","family":"Ioannides","sequence":"additional","affiliation":[{"name":"State Health Services Organization, Aglantzia 2100, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1054-643X","authenticated-orcid":false,"given":"Lakis","family":"Palazis","sequence":"additional","affiliation":[{"name":"State Health Services Organization, Aglantzia 2100, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Vavlitou","sequence":"additional","affiliation":[{"name":"State Health Services Organization, Aglantzia 2100, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1574-1827","authenticated-orcid":false,"given":"Marios S.","family":"Pattichis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Constantinos S.","family":"Pattichis","sequence":"additional","affiliation":[{"name":"Centre of Excellence, CYENS, Nicosia 1016, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas S.","family":"Panayides","sequence":"additional","affiliation":[{"name":"Centre of Excellence, CYENS, Nicosia 1016, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alexandropoulou, C.-A., Panagiotopoulos, I., Kleanthous, S., Dimitrakopoulos, G., Constantinou, I., Politi, E., Ntalaperas, D., Papageorgiou, X., Stylianides, C., and Ioannides, N. (2023, January 9\u201313). AI-Enabled Solutions, Explainability and Ethical Concerns for Predicting Sepsis in ICUs: A Systematic Review. Proceedings of the 2023 IEEE 19th International Conference on e-Science (e-Science), Limassol, Cyprus.","DOI":"10.1109\/e-Science58273.2023.10254863"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/JBHI.2020.2991043","article-title":"AI in Medical Imaging Informatics: Current Challenges and Future Directions","volume":"24","author":"Panayides","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_3","unstructured":"Doshi-Velez, F., and Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv."},{"key":"ref_4","first-page":"122120","article-title":"Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research","volume":"186 Pt A","author":"Haque","year":"2022","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_6","unstructured":"Holzinger, A., Biemann, C., Pattichis, C.S., and Kell, D.B. (2017). What do we need to build explainable AI systems for the medical domain?. arXiv."},{"key":"ref_7","first-page":"5176705","article-title":"Breast cancer detection in the Iot health environment using modified recursive feature selection","volume":"2019","author":"Memon","year":"2019","journal-title":"Wirel. Commun. Mob."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.21873\/anticanres.14482","article-title":"Artificial Intelligence in Ovarian Cancer Diagnosis","volume":"40","author":"Akazawa","year":"2020","journal-title":"Anticancer Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Azar, A.S., Rikan, S.B., Naemi, A., Mohasefi, J.B., Pirnejad, H., Mohasefi, M.B., and Wiil, U.K. (2022). Application of machine learning techniques for predicting survival in ovarian cancer. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-02087-y"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Khourdifi, Y., and Bahaj, M. (2018, January 5\u20136). Applying Best Machine Learning Algorithms for Breast Cancer Prediction and Classification. Proceedings of the 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), Kenitra, Morocco.","DOI":"10.1109\/ICECOCS.2018.8610632"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.procs.2021.07.062","article-title":"Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis","volume":"191","author":"Naji","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_12","first-page":"780","article-title":"Melanoma Skin Cancer Detection using Image Processing and Machine Learning","volume":"3","author":"Vijayalakshmi","year":"2019","journal-title":"Int.-Natl. J. Trend Sci. Res. Dev. (Ijtsrd)"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sun, W., Zheng, B., and Qian, W. (March, January 27). Computer aided lung cancer diagnosis with deep learning algorithms. Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, CA, USA.","DOI":"10.1117\/12.2216307"},{"key":"ref_14","first-page":"3204","article-title":"Machine learning and artificial intelligence based diabetes mellitus detection and self-management: A systematic review","volume":"34","author":"Chaki","year":"2020","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.aci.2018.12.004","article-title":"Predictive modelling and analytics for diabetes using a machine learning approach","volume":"18","author":"Kaur","year":"2022","journal-title":"Appl. Comput. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s13755-019-0095-z","article-title":"Classification and prediction of diabetes disease using machine learning paradigm","volume":"8","author":"Maniruzzaman","year":"2020","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s11596-019-2077-4","article-title":"Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults","volume":"39","author":"Xiong","year":"2019","journal-title":"Curr. Med. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Deberneh, H.M., and Kim, I. (2021). Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18063317"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"E130","DOI":"10.5888\/pcd16.190109","article-title":"Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques","volume":"16","author":"Xie","year":"2019","journal-title":"Prev. Chronic Dis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.artmed.2019.07.007","article-title":"Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes","volume":"98","author":"Woldaregay","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Narin, A., Isler, Y., and Ozer, M. (2016, January 27\u201329). Early prediction of Paroxysmal Atrial Fibrillation using frequency domain measures of heart rate variability. Proceedings of the 2016 Medical Technologies National Congress (TIPTEKNO), Antalya, Turkey.","DOI":"10.1109\/TIPTEKNO.2016.7863110"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s12553-020-00499-2","article-title":"Comparing different feature selection algorithms for cardiovascular disease prediction","volume":"11","author":"Hasan","year":"2020","journal-title":"Health Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1186\/s12933-022-01672-9","article-title":"Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: A machine learning approach","volume":"21","author":"Nabrdalik","year":"2022","journal-title":"Cardiovasc. Diabetol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s42979-020-00365-y","article-title":"Heart Disease Prediction using Machine Learning Techniques","volume":"1","author":"Shah","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bhatt, C.M., Patel, P., Ghetia, T., and Mazzeo, P.L. (2023). Effective Heart Disease Prediction Using Machine Learning Techniques. Algorithms, 16.","DOI":"10.3390\/a16020088"},{"key":"ref_26","first-page":"3860146","article-title":"A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms","volume":"8","author":"Haq","year":"2018","journal-title":"Mob. Inf. Syst."},{"key":"ref_27","first-page":"261","article-title":"Implementation of Machine Learning Model to Predict Heart Failure Disease","volume":"10","author":"Alotaibi","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.future.2019.10.043","article-title":"HealthFog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments","volume":"104","author":"Tuli","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_29","first-page":"4442","article-title":"Improved heart disease diagnostic IoT model using machine learning techniques","volume":"9","author":"Isravel","year":"2020","journal-title":"Neuroscience"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Painuli, D., Mishra, D., Bhardwaj, S., and Aggarwal, M. (2021). Forecast and prediction of COVID-19 using machine learning. Data Science for COVID-19, Academic Press.","DOI":"10.1016\/B978-0-12-824536-1.00027-7"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Stylianides, C., Malialis, K., and Kolios, P.A. (2023). Study of Data-Driven Methods for Adaptive Forecasting of COVID-19 Cases. International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-031-44207-0_6"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s12518-021-00365-4","article-title":"COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: A case study for Iraq","volume":"13","author":"Yahya","year":"2021","journal-title":"Appl. Geomat."},{"key":"ref_33","first-page":"933","article-title":"AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States Vs. Union Territories","volume":"67","author":"Gupta","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5033","DOI":"10.1038\/s41467-020-18684-2","article-title":"Machine learning based early warning system enables accurate mortality risk prediction for COVID-19","volume":"11","author":"Gao","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100178","DOI":"10.1016\/j.smhl.2020.100178","article-title":"Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making","volume":"20","author":"Pourhomayoun","year":"2021","journal-title":"Smart Health"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1097\/CCM.0000000000004145","article-title":"Early prediction of sepsis from clinical data: The PhysioNet\/Computing in Cardiology Challenge 2019","volume":"48","author":"Reyna","year":"2020","journal-title":"Crit. Care Med."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Olang, O., Mohseni, S., Shahabinezhad, A., Hamidianshirazi, Y., Goli, A., Abolghasemian, M., Shafiee, M.A., Aarabi, M., Alavinia, M., and Shaker, P. (2024). Artificial Intelligence-Based Models for Prediction of Mortality in ICU Patients: A Scoping Review. J. Intensiv. Care Med., 08850666241277134.","DOI":"10.1177\/08850666241277134"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1093\/jamia\/ocab236","article-title":"Sepsis prediction, early detection, and identification using clinical text for machine learning: A systematic review","volume":"29","author":"Yan","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Moor, M., Rieck, B., Horn, M., Jutzeler, C.R., and Borgwardt, K. (2021). Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review. Front. Med., 8.","DOI":"10.3389\/fmed.2021.607952"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s00134-019-05872-y","article-title":"Machine learning for the prediction of sepsis: A systematic review and meta-analysis of diagnostic test accuracy","volume":"46","author":"Fleuren","year":"2020","journal-title":"Intensiv. Care Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yadgarov, M.Y., Landoni, G., Berikashvili, L.B., Polyakov, P.A., Kadantseva, K.K., Smirnova, A.V., Kuznetsov, I.V., Shemetova, M.M., Yakovlev, A.A., and Likhvantsev, V.V. (2024). Early detection of sepsis using machine learning algorithms: A systematic review and network me-ta-analysis. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1491358"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Islam, K.R., Prithula, J., Kumar, J., Tan, T.L., Reaz, M.B.I., Sumon, S.I., and Chowdhury, M.E.H. (2023). Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review. J. Clin. Med., 12.","DOI":"10.3390\/jcm12175658"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"122982","DOI":"10.1016\/j.eswa.2023.122982","article-title":"Systematic review and network meta-analysis of machine learning algorithms in sepsis prediction","volume":"245","author":"Gao","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_44","first-page":"e59797","article-title":"Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool","volume":"16","author":"Suresh","year":"2024","journal-title":"Cureus"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1067\/mcp.2001.113989","article-title":"Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework","volume":"69","author":"Atkinson","year":"2001","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1111\/1742-6723.13126","article-title":"Deliberate clinical inertia: Using meta-cognition to improve decision-making","volume":"30","author":"Keijzers","year":"2018","journal-title":"Emerg. Med. Australas."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"407","DOI":"10.5694\/mja16.00999","article-title":"Countering cognitive biases in minimising low value care","volume":"206","author":"Soon","year":"2017","journal-title":"Med. J. Aust."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s11908-021-00747-0","article-title":"Host Diagnostic Biomarkers of Infection in the ICU: Where Are We and Where Are We Going?","volume":"23","author":"Heffernan","year":"2021","journal-title":"Curr. Infect. Dis. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"33","DOI":"10.4103\/ajim.ajim_18_22","article-title":"C-Reactive Protein and Lactate Dehydrogenase in Intensive Care Unit and Nonintensive Care Unit COVID-19 Patients\u2014A Retrospective Study","volume":"11","author":"Astagimath","year":"2023","journal-title":"APIK J. Intern. Med."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"641039","DOI":"10.1155\/2014\/641039","article-title":"Serum soluble triggering receptor expressed on myeloid cells-1 and pro-calcitonin can reflect sepsis severity and predict prognosis: A prospective cohort study","volume":"2014","author":"Li","year":"2014","journal-title":"Mediat. Inflamm."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.jinf.2008.02.015","article-title":"A prospective evaluation of the Infection Probability Score (IPS) in the intensive care unit","volume":"56","author":"Martini","year":"2008","journal-title":"J. Infect."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Neocleous, A., Papaioannou, M., Savva, P., Miguel, F., Panayides, A., Antoniou, Z., Neofytou, M., Schiza, E.C., Neokleous, K., and Constantinou, I. (2022, January 17\u201319). The International Patient Summary: Proposal for a National Implementation for Cyprus. Proceedings of the 2022 E-Health and Bioengineering Conference (EHB), Iasi, Romania.","DOI":"10.1109\/EHB55594.2022.9991445"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1159\/000113106","article-title":"Estimation of the Mortality Risk of Surgical Intensive Care Patients Based on Routine Laboratory Parameters","volume":"40","author":"Stachon","year":"2008","journal-title":"Eur. Surg. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1177\/0310057X0903700217","article-title":"An Update on C-reactive Protein for Intensivists","volume":"37","author":"Ho","year":"2009","journal-title":"Anaesth. Intensiv. Care"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Qu, R., Hu, L., Ling, Y., Hou, Y., Fang, H., Zhang, H., Liang, S., He, Z., Fang, M., and Li, J. (2020). C-reactive protein concentration as a risk predictor of mortality in intensive care unit: A multicenter, prospective, observational study. BMC Anesthesiol., 20.","DOI":"10.1186\/s12871-020-01207-3"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"R63","DOI":"10.1186\/cc4892","article-title":"Early identification of intensive care unit-acquired infections with daily monitoring of C-reactive proHpective observational study","volume":"10","author":"Coelho","year":"2006","journal-title":"Crit. Care"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s15010-019-01383-6","article-title":"Using the kinetics of C-reactive protein response to improve the differential diagnosis between acute bacterial and viral infections","volume":"48","author":"Coster","year":"2020","journal-title":"Infection"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1515\/cclm-2015-0277","article-title":"Neutrophil CD64 combined with PCT, CRP and WBC improves the sensitivity for the early diagnosis of neonatal sepsis","volume":"54","author":"Yang","year":"2016","journal-title":"Clin. Chem. Lab. Med. (CCLM)"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Henriquez-Camacho, C., and Losa, J. (2014). Biomarkers for Sepsis. BioMed Res. Int., 2014.","DOI":"10.1155\/2014\/547818"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S1388-9842(02)00021-1","article-title":"C-reactive protein as a predictor of improvement and readmission in heart failure","volume":"4","year":"2002","journal-title":"Eur. J. Heart Fail."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1378\/chest.123.6.2043","article-title":"C-reactive protein levels correlate with mortality and organ failure in critically ill patients","volume":"123","author":"Lobo","year":"2003","journal-title":"Chest"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.jcrc.2006.01.005","article-title":"C-reactive protein concentration as a predictor of intensive care unit readmission: A nested case-control study","volume":"21","author":"Ho","year":"2006","journal-title":"J. Crit. Care"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s00134-007-0928-0","article-title":"C-reactive protein concentration as a predictor of in-hospital mortality after ICU discharge: A prospective cohort study","volume":"34","author":"Ho","year":"2007","journal-title":"Intensiv. Care Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1001\/jama.288.8.980","article-title":"Inflammatory biomarkers, hormone replacement therapy, and incident coronary heart disease: Pro-spective analysis from the Women\u2019s Health Initiative observational study","volume":"288","author":"Pradhan","year":"2002","journal-title":"JAMA"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s00520-007-0381-1","article-title":"Non-infectious causes of elevated procalcitonin and C-reactive protein serum levels in pediatric patients with hematologic and oncologic disorders","volume":"16","author":"Dornbusch","year":"2008","journal-title":"Support. Care Cancer"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Meyer, Z.C., Schreinemakers, J.M.J., Mulder, P.G.H., de Waal, R.A.L., Ermens, A.A.M., and van der Laan, L. (2013). The Role of C-Reactive Protein and the SOFA Score as Parameter for Clinical Decision Making in Surgical Patients during the Intensive Care Unit Course. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0055964"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"A725","DOI":"10.1016\/j.chest.2022.08.571","article-title":"Correlation of c-reactive protein (crp) with icu COVID-19 ards mortality in adults","volume":"162","author":"Chandran","year":"2022","journal-title":"Chest"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/0009-8981(81)90005-X","article-title":"Solid phase radioimmunoassays for human C-reactive protein","volume":"117","author":"Shine","year":"1981","journal-title":"Clin. Chim. Acta"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s15010-007-7077-9","article-title":"The Time Course of Blood C-reactive Protein Concentrations in Relation to the Response to Initial Antimicrobial Therapy in Patients with Sepsis","volume":"36","author":"Schmit","year":"2008","journal-title":"Infection"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1097\/00003246-200203000-00006","article-title":"Diagnosis and follow-up of infections in intensive care patients: Value of C-reactive protein compared with other clinical and biological variables*","volume":"30","author":"Reny","year":"2002","journal-title":"Crit. Care Med."},{"key":"ref_71","first-page":"61","article-title":"C reactive protein as marker of infection among patients with severe closed trauma","volume":"19","author":"Flores","year":"2001","journal-title":"Enfermedades Infecc. Microbiol. Clin."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13613-016-0105-0","article-title":"Elevated C-reactive protein levels at ICU discharge as a predictor of ICU outcome: A retrospective cohort study","volume":"6","author":"Bruins","year":"2016","journal-title":"Ann. Intensiv. Care"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"8997709","DOI":"10.1155\/2022\/8997709","article-title":"D-dimer, CRP, PCT, and IL-6 Levels at Admission to ICU Can Predict In-Hospital Mortality in Patients with COVID-19 Pneumonia","volume":"2022","author":"Milenkovic","year":"2022","journal-title":"Oxidative Med. Cell. Longev."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Picod, A., Morisson, L., de Roquetaillade, C., Sadoune, M., Mebazaa, A., Gayat, E., Davison, B.A., Cotter, G., and Chousterman, B.G. (2022). Systemic Inflammation Evaluated by Interleukin-6 or C-Reactive Protein in Critically Ill Patients: Results from the FROG-ICU Study. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.868348"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1038\/ni.3153","article-title":"IL-6 as a keystone cytokine in health and disease","volume":"16","author":"Hunter","year":"2015","journal-title":"Nat. Immunol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1084\/jem.169.1.333","article-title":"The complex pattern of cytokines in serum from patients with meningococcal septic shock. Association between interleukin 6, interleukin 1, and fatal outcome","volume":"169","author":"Waage","year":"1989","journal-title":"J. Exp. Med."},{"key":"ref_77","first-page":"439","article-title":"Diagnostic and prognostic value of very high serum lactate dehydrogenase in admitted medical patients","volume":"16","author":"Erez","year":"2014","journal-title":"Isr. Med. Assoc. J."},{"key":"ref_78","first-page":"4265","article-title":"Clinical significance of the detection of procalcitonin and C-reactive protein in the intensive care unit","volume":"15","author":"Li","year":"2018","journal-title":"Exp. Ther. Med."},{"key":"ref_79","first-page":"939","article-title":"Diagnostic value of C-reactive protein and procalcitonin for bacterial infection in acute exacerbations of chronic obstructive pulmonary disease","volume":"39","author":"Zhang","year":"2014","journal-title":"Zhong Nan Da Xue Xue Bao. Yi Xue Ban J. Cent. South Univ. Med. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"ofaa096","DOI":"10.1093\/ofid\/ofaa096","article-title":"Low Sensitivity of Procalcitonin for Bacteremia at an Academic Medical Center: A Cautionary Tale for Antimicrobial Stewardship","volume":"7","author":"Goodlet","year":"2020","journal-title":"Open Forum Infect. Dis."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1177\/0885066610377980","article-title":"Review of A Large Clinical Series: Is the Band Count Useful in the Diagnosis of Infection? An Accuracy Study in Critically Ill Patients","volume":"25","author":"Cavallazzi","year":"2010","journal-title":"J. Intensiv. Care Med."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ljungstr\u00f6m, L., Pernestig, A.-K., Jacobsson, G., Andersson, R., Usener, B., and Tilevik, D. (2017). Diagnostic accuracy of procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, and lactate in patients with suspected bacterial sepsis. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0181704"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Westerdijk, K., Simons, K.S., Zegers, M., Wever, P.C., Pickkers, P., and de Jager, C.P.C. (2019). The value of the neutrophil-lymphocyte count ratio in the diagnosis of sepsis in patients admitted to the Intensive Care Unit: A retrospective cohort study. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0212861"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s00134-022-06914-8","article-title":"ICU scoring systems","volume":"49","author":"Quintairos","year":"2023","journal-title":"Intensiv. Care Med."},{"key":"ref_85","unstructured":"Jaganath, U. (2024, July 01). An Overview of Predictive Scoring Systems Used in ICU. Available online: https:\/\/anaesthetics.ukzn.ac.za\/wp-content\/uploads\/2020\/07\/05-June-2020-An-overview-of-predictive-scoring-systems-used-in-ICU-U-Jaganath.pdf."},{"key":"ref_86","unstructured":"Kelley, M.A., Manaker, S., and Finlay, G. (2024, July 01). Predictive Scoring Systems in the Intensive Care Unit. UpToDate. Available online: http:\/\/www.uptodate.com\/online\/content\/author.do."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0749-0704(18)30141-6","article-title":"Predicting Intensive Care Unit Outcome with Scoring Systems: Underlying Concepts and Principles","volume":"10","author":"Kollef","year":"1994","journal-title":"Crit. Care Clin."},{"key":"ref_88","first-page":"220","article-title":"Scoring systems in the intensive care unit: A compendium","volume":"18","author":"Rapsang","year":"2014","journal-title":"Indian J. Crit. Care Med. Peer-Rev. Off. Publ. Indian Soc. Crit. Care Med."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"453","DOI":"10.4111\/icu.2017.58.6.453","article-title":"Validation of APACHE II scoring system at 24 hours after admission as a prognostic tool in urosepsis: A prospective observational study","volume":"58","author":"VijayGanapathy","year":"2017","journal-title":"Investig. Clin. Urol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1097\/01.CCM.0000215112.84523.F0","article-title":"Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today\u2019s critically ill patients*","volume":"34","author":"Zimmerman","year":"2006","journal-title":"Crit. Care Med."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1001\/jama.1990.03450180053028","article-title":"Admission source to the medical intensive care unit predicts hospital death independent of APACHE II score","volume":"264","author":"Escarce","year":"1990","journal-title":"JAMA"},{"key":"ref_92","first-page":"7","article-title":"A comparison of APACHE II and APACHE IV scoring systems in predicting outcome in patients admitted with stroke to an intensive care unit","volume":"15","author":"Ayazoglu","year":"2011","journal-title":"Anaesth. Pain Intensiv. Care"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1378\/chest.08-2591","article-title":"Mortality probability model III and simplified acute physiology score II: Assessing their value in predicting length of stay and comparison to APACHE IV","volume":"136","author":"Vasilevskis","year":"2009","journal-title":"Chest"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"102","DOI":"10.4266\/acc.2018.00185","article-title":"Scoring Systems for the Patients of Intensive Care Unit","volume":"33","author":"Jeong","year":"2018","journal-title":"Acute Crit. Care"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.18203\/2320-6012.ijrms20191643","article-title":"The comparison of apache II and apache IV score to predict mortality in intensive care unit in a tertiary care hospital","volume":"7","author":"Nagar","year":"2019","journal-title":"Int. J. Res. Med. Sci."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s13054-017-1930-8","article-title":"Performance of critical care prognostic scoring systems in low and middle-income countries: A systematic review","volume":"22","author":"Haniffa","year":"2018","journal-title":"Crit. Care"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1001\/jama.286.14.1754","article-title":"Serial Evaluation of the SOFA Score to Predict Outcome in Critically Ill Patients","volume":"286","author":"Ferreira","year":"2001","journal-title":"JAMA"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1186\/s13054-019-2663-7","article-title":"The SOFA score\u2014Development, utility and challenges of accurate assessment in clinical trials","volume":"23","author":"Lambden","year":"2019","journal-title":"Crit. Care"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1093\/bjaceaccp\/mkn033","article-title":"Severity scoring systems in the critically ill","volume":"8","author":"Bouch","year":"2008","journal-title":"Contin. Educ. Anaesth. Crit. Care Pain"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1001\/jama.1993.03510240069035","article-title":"A New Simplified Acute Physiology Score (SAPS II) Based on a European\/North American Multicenter Study","volume":"270","author":"Lemeshow","year":"1993","journal-title":"JAMA"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1007\/s00134-005-2763-5","article-title":"SAPS 3\u2014From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission","volume":"31","author":"Moreno","year":"2005","journal-title":"Intensiv. Care Med."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s00134-007-0690-3","article-title":"Variability in outcome and resource use in intensive care units","volume":"33","author":"Rothen","year":"2007","journal-title":"Intensiv. Care Med."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1097\/01.CCM.0000257337.63529.9F","article-title":"Assessing contemporary intensive care unit outcome: An updated Mortality Probability Admission Model (MPM0-III)*","volume":"35","author":"Higgins","year":"2007","journal-title":"Crit. Care Med."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1097\/CCM.0000000000002936","article-title":"An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU","volume":"46","author":"Nemati","year":"2018","journal-title":"Crit. Care Med."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1007\/s00134-017-5034-3","article-title":"What\u2019s New in ICU in 2050: Big Data and Machine Learning","volume":"44","author":"Bailly","year":"2018","journal-title":"Intensiv. Care Med."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s001340050992","article-title":"Application of mortality prediction systems to individual intensive care units","volume":"25","author":"Patel","year":"1999","journal-title":"Intensiv. Care Med."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"423.e1","DOI":"10.1016\/j.jcrc.2011.08.016","article-title":"Caution when using prognostic models: A prospective comparison of 3 recent prognostic models","volume":"27","author":"Nassar","year":"2012","journal-title":"J. Crit. Care"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1097\/01.CCM.0000201881.58644.41","article-title":"Mortality predictions in the intensive care unit: Comparing physicians with scoring systems*","volume":"34","author":"Sinuff","year":"2006","journal-title":"Crit. Care Med."},{"key":"ref_110","unstructured":"Yang, J., Karstens, L., Ross, C., and Yala, A. (2022). AI gone astray: Technical supplement. arXiv."},{"key":"ref_111","unstructured":"Van De Water, R., Schmidt, H., Elbers, P., Thoral, P., Arnrich, B., and Rockenschaub, P. (2023). Yet another icu benchmark: A flexible multi-center framework for clinical ml. arXiv."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s41746-021-00504-6","article-title":"Artificial intelligence sepsis prediction algorithm learns to say \u201cI don\u2019t know\u201d","volume":"4","author":"Shashikumar","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1186\/s12967-020-02620-5","article-title":"Predicting 30-days mortality for MIMIC-III patients with sepsis-3: A machine learning approach using XGboost","volume":"18","author":"Hou","year":"2020","journal-title":"J. Transl. Med."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Kong, G., Lin, K., and Hu, Y. (2020). Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01271-2"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"e28000","DOI":"10.2196\/28000","article-title":"A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study","volume":"5","author":"Persson","year":"2021","journal-title":"JMIR Form. Res."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"104176","DOI":"10.1016\/j.ijmedinf.2020.104176","article-title":"The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit","volume":"141","author":"Yuan","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Wang, D., Li, J., Sun, Y., Ding, X., Zhang, X., Liu, S., Han, B., Wang, H., Duan, X., and Sun, T. (2021). A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients. Front. Public Health, 9.","DOI":"10.3389\/fpubh.2021.754348"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Kok, C., Jahmunah, V., Oh, S.L., Zhou, X., Gururajan, R., Tao, X., Cheong, K.H., Gururajan, R., Molinari, F., and Acharya, U. (2020). Automated prediction of sepsis using temporal convolutional network. Comput. Biol. Med., 127.","DOI":"10.1016\/j.compbiomed.2020.103957"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"6522633","DOI":"10.1155\/2021\/6522633","article-title":"Early Prediction of Sepsis Based on Machine Learning Algorithm","volume":"2021","author":"Zhao","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Ghias, N., Ul Haq, S., Arshad, H., Sultan, H., Bashir, F., Ghaznavi, S.A., Shabbir, M., Badshah, Y., and Rafiq, M. (2022). Using Machine Learning Algorithms to predict sepsis and its stages in ICU patients. medRxiv.","DOI":"10.1101\/2022.03.15.22271655"},{"key":"ref_121","unstructured":"Moor, M., Horn, M., Rieck, B., Roqueiro, D., and Borgwardt, K. (2019, January 9\u201310). Early recognition of sepsis with Gaussian process temporal convolutional networks and dynamic time warping. Proceedings of the Machine Learning for Healthcare Conference, Ann Arbor, MI, USA."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Cruz, M.F., Ono, N., Huang, M., Amin, A.U., Kanaya, S., and Cavalcante, C.A.M.T. (2021). Kinematics approach with neural networks for early detection of sepsis (KANNEDS). BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01529-3"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.2147\/JIR.S441591","article-title":"Machine Learning Predictive Model for Septic Shock in Acute Pancreatitis with Sepsis","volume":"17","author":"Xia","year":"2024","journal-title":"J. Inflamm. Res."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Lin, P.-C., Chen, K.-T., Chen, H.-C., Islam, M., and Lin, M.-C. (2021). Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation. J. Pers. Med., 11.","DOI":"10.3390\/jpm11111055"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Burdick, H., Pino, E., Gabel-Comeau, D., Gu, C., Roberts, J., Le, S., Slote, J., Saber, N., Pellegrini, E., and Green-Saxena, A. (2020). Validation of a machine learning algorithm for early severe sepsis prediction: A retrospective study pre-dicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01284-x"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Yong, L., and Zhenzhou, L. (2024). Deep learning-based prediction of in-hospital mortality for sepsis. Sci. Rep., 14.","DOI":"10.1038\/s41598-023-49890-9"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Ghiasi, S., Zhu, T., Lu, P., Hagenah, J., Khanh, P.N.Q., Van Hao, N., Thwaites, L., Clifton, D.A., and Vital Consortium (2022). Sepsis Mortality Prediction Using Wearable Monitoring in Low\u2013Middle Income Countries. Sensors, 22.","DOI":"10.3390\/s22103866"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Kim, T., Tae, Y., Yeo, H.J., Jang, J.H., Cho, K., Yoo, D., Lee, Y., Ahn, S.-H., Kim, Y., and Lee, N. (2023). Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data. J. Clin. Med., 12.","DOI":"10.3390\/jcm12227156"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"101236","DOI":"10.1016\/j.imu.2023.101236","article-title":"Comparison of different machine learning algorithms to classify patients suspected of having sepsis infection in the intensive care unit","volume":"38","author":"Gholamzadeh","year":"2023","journal-title":"Inform. Med. Unlocked"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1038\/s41467-021-20910-4","article-title":"Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare","volume":"12","author":"Goh","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1093\/jamiaopen\/ooaa006","article-title":"Machine learning for early detection of sepsis: An internal and temporal validation study","volume":"3","author":"Bedoya","year":"2020","journal-title":"JAMIA Open"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"101820","DOI":"10.1016\/j.artmed.2020.101820","article-title":"Early detection of sepsis utilizing deep learning on electronic health record event sequences","volume":"104","author":"Lauritsen","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Scherpf, M., Gr\u00e4\u00dfer, F., Malberg, H., and Zaunseder, S. (2019). Predicting sepsis with a recurrent neural network using the MIMIC III database. Comput. Biol. Med., 113.","DOI":"10.1016\/j.compbiomed.2019.103395"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Li, X., Ng, G.A., and Schlindwein, F. (2019, January 8\u201311). Convolutional and Recurrent Neural Networks for Early Detection of Sepsis Using Hourly Physiological Data from Patients in Intensive Care Unit. Proceedings of the 2019 Computing in Cardiology Conference, Singapore.","DOI":"10.22489\/CinC.2019.054"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"100196","DOI":"10.1016\/j.patter.2020.100196","article-title":"An interpretable deep-learning model for early prediction of sepsis in the emergency department","volume":"2","author":"Zhang","year":"2021","journal-title":"Patterns"},{"key":"ref_136","first-page":"100042","article-title":"Towards early sepsis detection from measurements at the general ward through deep learning","volume":"5","author":"Oei","year":"2021","journal-title":"Intell. Med."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Kamal, S.A., Yin, C., Qian, B., and Zhang, P. (2020). An interpretable risk prediction model for healthcare with pattern attention. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01331-7"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Krissaane, I., Hampton, K., Alshenaifi, J., and Wilkinson, R. (2019, January 8\u201311). Anomaly Detection Semi-Supervised Framework for Sepsis Treatment. Proceedings of the 2019 Computing in Cardiology Conference (CinC), Singapore.","DOI":"10.22489\/CinC.2019.174"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"17903","DOI":"10.1007\/s10489-022-04425-z","article-title":"Early prediction of sepsis using double fusion of deep features and handcrafted features","volume":"53","author":"Duan","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_140","first-page":"5442","article-title":"A Deep Learning-Based Sepsis Estimation Scheme","volume":"9","author":"Lu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"e49784","DOI":"10.2196\/49784","article-title":"Sepsis Prediction at Emergency Department Triage Using Natural Language Processing: Retrospective Cohort Study","volume":"3","author":"Brann","year":"2024","journal-title":"JMIR AI"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Choi, J.S., Trinh, T.X., Ha, J., Yang, M.S., Lee, Y., Kim, Y.E., Choi, J., Byun, H.G., Song, J., and Yoon, T.H. (2020). Implementation of complementary model using optimal combination of hematological parameters for sepsis screening in patients with fever. Sci. Rep., 10.","DOI":"10.1038\/s41598-019-57107-1"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Fagerstr\u00f6m, J., B\u00e5ng, M., Wilhelms, D., and Chew, M.S. (2019). LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-51219-4"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Bai, Y., Xia, J., Huang, X., Chen, S., and Zhan, Q. (2022). Using machine learning for the early prediction of sepsis-associated ARDS in the ICU and identification of clinical phenotypes with differential responses to treatment. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.1050849"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Misra, D., Avula, V., Wolk, D.M., Farag, H.A., Li, J., Mehta, Y.B., Sandhu, R., Karunakaran, B., Kethireddy, S., and Zand, R. (2021). Early Detection of Septic Shock Onset Using Interpretable Machine Learners. J. Clin. Med., 10.","DOI":"10.3390\/jcm10020301"},{"key":"ref_146","unstructured":"Zhao, Y., Qiao, Z., Xiao, C., Glass, L., and Sun, J. (2021). Pyhealth: A python library for health predictive models. arXiv."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Mandyam, A., Yoo, E.C., Soules, J., Laudanski, K., and Engelhardt, B.E. (2021, January 1\u20134). COP-E-CAT: Cleaning and organization pipeline for EHR computational and analytic tasks. Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Gainesville, FL, USA.","DOI":"10.1145\/3459930.3469536"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Guo, C., Lu, M., and Chen, J. (2020). An evaluation of time series summary statistics as features for clinical prediction tasks. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-1063-x"},{"key":"ref_150","unstructured":"Y\u00e8che, H., Kuznetsova, R., Zimmermann, M., H\u00fcser, M., Lyu, X., Faltys, M., and R\u00e4tsch, G. (2021). HiRID-ICU-Benchmark\u2013A Comprehensive Machine Learning Benchmark on High-resolution ICU Data. arXiv."},{"key":"ref_151","first-page":"e21347","article-title":"Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation","volume":"9","author":"Alghatani","year":"2021","journal-title":"JMIR Public Health Surveill."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41597-019-0103-9","article-title":"Multitask learning and benchmarking with clinical time series data","volume":"6","author":"Harutyunyan","year":"2019","journal-title":"Sci. Data"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Sheikhalishahi, S., Balaraman, V., and Osmani, V. (2020). Benchmarking machine learning models on multi-centre eICU critical care dataset. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0235424"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Dan, T., Li, Y., Zhu, Z., Chen, X., Quan, W., Hu, Y., Tao, G., Zhu, L., Zhu, J., and Jin, Y. (2020, January 16\u201319). Machine learning to predict ICU admission, ICU mortality and survivors\u2019 length of stay among COVID-19 patients: Toward optimal allocation of ICU resources. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Bio-Medicine (BIBM), Seoul, Republic of Korea.","DOI":"10.2139\/ssrn.3631305"},{"key":"ref_155","unstructured":"Rocheteau, E., Tong, C., Veli\u010dkovi\u0107, P., Lane, N., and Li\u00f2, P. (2021). Predicting patient outcomes with graph representation learning. arXiv."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Scheltjens, V., Momo, L.N.W., Verbeke, W., and De Moor, B. (2023). Client Recruitment for Federated Learning in ICU Length of Stay Prediction. arXiv.","DOI":"10.1109\/e-Science58273.2023.10254908"},{"key":"ref_157","first-page":"100365","article-title":"Time-to-event modeling for hospital length of stay prediction for COVID-19 patients","volume":"9","author":"Wen","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s11517-021-02327-9","article-title":"Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks","volume":"59","author":"Dogu","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Lefering, R., Waydhas, C., and DGU, T. (2024). Prediction of prolonged length of stay on the intensive care unit in severely injured patients\u2014A registry-based multivariable analysis. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1358205"},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Abdurrab, I., Mahmood, T., Sheikh, S., Aijaz, S., Kashif, M., Memon, A., Ali, I., Peerwani, G., Pathan, A., and Alkhodre, A.B. (2024). Predicting the length of stay of cardiac patients based on pre-operative variables\u2014Bayesian models vs. machine learning models. Healthcare, 12.","DOI":"10.3390\/healthcare12020249"},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Chen, J., Wen, Y., Pokojovy, M., Tseng, T.-L., McCaffrey, P., Vo, A., Walser, E., and Moen, S. (2024). Multi-modal learning for inpatient length of stay prediction. Comput. Biol. Med., 171.","DOI":"10.1016\/j.compbiomed.2024.108121"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Karankot, M.I., Marceau, M., Glenn, E.M., Fowers, R.P., Hedges, D.M., Sheehey, B., and Whitaker, B.M. (2024, January 13\u201314). Addressing the Challenge of Missing Medical Data in Healthcare Analytics: A Focus on Machine Learning Predictions for ICU Length of Stay. Proceedings of the 2024 Intermountain Engineering, Technology and Computing (IETC), Logan, UT, USA.","DOI":"10.1109\/IETC61393.2024.10564463"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"e25406","DOI":"10.1016\/j.heliyon.2024.e25406","article-title":"Predictors of in-ICU length of stay among congenital heart defect patients using artificial intelligence model: A pilot study","volume":"10","author":"Junior","year":"2024","journal-title":"Heliyon"},{"key":"ref_164","first-page":"5519","article-title":"Robust Length of Stay Prediction Model for Indoor Patients","volume":"70","author":"Siddiqa","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"104572","DOI":"10.1016\/j.ijmedinf.2021.104572","article-title":"The application of machine learning algorithms in predicting the length of stay following femoral neck fracture","volume":"155","author":"Zhong","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"104670","DOI":"10.1016\/j.ijmedinf.2021.104670","article-title":"Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty","volume":"158","author":"Abbas","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"104569","DOI":"10.1016\/j.ijmedinf.2021.104569","article-title":"Predicting length of stay and mortality among hospitalized patients with type 2 diabetes mellitus and hypertension","volume":"154","author":"Barsasella","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Su, L., Xu, Z., Chang, F., Ma, Y., Liu, S., Jiang, H., Wang, H., Li, D., Chen, H., and Zhou, X. (2021). Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models. Front. Med., 8.","DOI":"10.3389\/fmed.2021.664966"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Wu, J., Lin, Y., Li, P., Hu, Y., Zhang, L., and Kong, G. (2021). Predicting Prolonged Length of ICU Stay through Machine Learning. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122242"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s10479-022-04984-x","article-title":"Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients","volume":"328","author":"Saadatmand","year":"2022","journal-title":"Ann. Oper. Res."},{"key":"ref_171","first-page":"100052","article-title":"Predicting prolonged length of stay in patients with traumatic brain injury: A machine learning approach","volume":"6","author":"Abujaber","year":"2022","journal-title":"Intell. Med."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Wang, K., Yan, L.Z., Li, W.Z., Jiang, C., Ni Wang, N., Zheng, Q., Dong, N.G., and Shi, J.W. (2022). Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.863642"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Alsinglawi, B., Alshari, O., Alorjani, M., Mubin, O., Alnajjar, F., Novoa, M., and Darwish, O. (2022). An explainable machine learning framework for lung cancer hospital length of stay prediction. Sci. Rep., 12.","DOI":"10.1038\/s41598-021-04608-7"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Iwase, S., Nakada, T.-A., Shimada, T., Oami, T., Shimazui, T., Takahashi, N., Yamabe, J., Yamao, Y., and Kawakami, E. (2022). Prediction algorithm for ICU mortality and length of stay using machine learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-17091-5"},{"key":"ref_175","first-page":"311","article-title":"An Extensive Data Processing Pipeline for MIMIC-IV","volume":"193","author":"Gupta","year":"2022","journal-title":"Mach. Learn. Health"},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Wang, S., McDermott, M.B., Chauhan, G., Ghassemi, M., Hughes, M.C., and Naumann, T. (2020, January 2\u20134). Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii. Proceedings of the ACM Conference on Health, Inference, and Learning, Toronto, ON, Canada.","DOI":"10.1145\/3368555.3384469"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"443","DOI":"10.21037\/atm.2020.03.147","article-title":"Clinical characteristics of Coronavirus Disease 2019 and development of a prediction model for prolonged hospital length of stay","volume":"8","author":"Hong","year":"2020","journal-title":"Ann. Transl. Med."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Kuo, T.-T. (2024). Early prediction of long hospital stay for Intensive Care units readmission patients using medication information. Comput. Biol. Med., 174.","DOI":"10.1016\/j.compbiomed.2024.108451"},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Belmonte, E.M., Oton-Tortosa, S., Gutierrez-Martinez, J.-M., and Castillo-Martinez, A. (2024). An Intelligent Model and Methodology for Predicting Length of Stay and Survival in a Critical Care Hospital Unit. Informatics, 11.","DOI":"10.3390\/informatics11020034"},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Chen, Q., Zhang, B., Yang, J., Mo, X., Zhang, L., Li, M., Chen, Z., Fang, J., Wang, F., and Huang, W. (2021). Predicting Intensive Care Unit Length of Stay After Acute Type A Aortic Dissection Surgery Using Machine Learning. Front. Cardiovasc. Med., 8.","DOI":"10.3389\/fcvm.2021.675431"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"3691","DOI":"10.1016\/j.asej.2021.02.018","article-title":"Predicting length of stay in hospitals intensive care unit using general admission features","volume":"12","author":"Eltahawi","year":"2021","journal-title":"Ain Shams Eng. J."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"100937","DOI":"10.1016\/j.imu.2022.100937","article-title":"Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia","volume":"30","author":"Alabbad","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1016\/j.procs.2024.04.245","article-title":"LoSNet: A Tailored Deep Neural Network Framework for Precise Length of Stay Prediction in Disease-Specific Hospitalization","volume":"235","author":"K","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Harerimana, G., Kim, J.W., and Jang, B. (2021). A deep attention model to forecast the Length Of Stay and the in-hospital mortality right on admission from ICD codes and demographic data. J. Biomed. Inform., 118.","DOI":"10.1016\/j.jbi.2021.103778"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"27121","DOI":"10.1007\/s11042-023-16474-8","article-title":"Machine learning model for healthcare investments predicting the length of stay in a hospital & mortality rate","volume":"83","author":"Bhadouria","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1378\/chest.11-0718","article-title":"Benchmark Data from More Than 240,000 Adults That Reflect the Current Practice of Critical Care in the United States","volume":"140","author":"Lilly","year":"2011","journal-title":"Chest"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci. Data"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"180178","DOI":"10.1038\/sdata.2018.178","article-title":"The eICU Collaborative Research Database, a freely available multi-center database for critical care research","volume":"5","author":"Pollard","year":"2018","journal-title":"Sci. Data"},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Sulaiman, W.A., Nicolaou, A., Prentza, N., Stylianides, C., Panayides, A., Constantinou, I., Antoniou, Z., Kakas, A., Kyriacou, E., and Palazis, L. (2023, January 7\u20139). Emergency Department Triage Hospitalization Prediction Based on Machine Learning and Rule Ex-traction. Proceedings of the 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, St. Julians, Malta.","DOI":"10.1109\/IEEECONF58974.2023.10405176"},{"key":"ref_190","unstructured":"Xie, F., Zhou, J., Lee, J.W., Tan, M., Li, S., Rajnthern, L.S., Chee, M.L., Chakraborty, B., Wong, A.-K.I., and Dagan, A. (2023, December 04). Benchmarking Emergency Department Prediction Models with Machine Learning and Public Electronic Health Records. Nature News. Available online: https:\/\/www.nature.com\/articles\/s41597-022-01782-9."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/s13054-019-2351-7","article-title":"Emergency department triage prediction of clinical outcomes using machine learning models","volume":"23","author":"Raita","year":"2019","journal-title":"Crit. Care"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"e186937","DOI":"10.1001\/jamanetworkopen.2018.6937","article-title":"Machine learning\u2013based prediction of clinical out-comes for children during emergency department triage","volume":"2","author":"Goto","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"104163","DOI":"10.1016\/j.ijmedinf.2020.104163","article-title":"Predicting hospital admission for older emergency department patients: Insights from machine learning","volume":"140","author":"Mowbray","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"Lee, J.-T., Hsieh, C.-C., Lin, C.-H., Lin, Y.-J., and Kao, C.-Y. (2021). Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-98961-2"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ijpe.2018.11.024","article-title":"Predictive analytics for hospital admissions from the emergency department using triage information","volume":"208","author":"Araz","year":"2019","journal-title":"Int. J. Prod. Econ."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"10458","DOI":"10.1109\/ACCESS.2018.2808843","article-title":"Using Data Mining to Predict Hospital Admissions From the Emergency Department","volume":"6","author":"Graham","year":"2018","journal-title":"IEEE Access"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"117314","DOI":"10.1016\/j.eswa.2022.117314","article-title":"An integrated optimization and machine learning approach to predict the admission status of emergency patients","volume":"202","author":"Ahmed","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"104468","DOI":"10.1016\/j.ijmedinf.2021.104468","article-title":"Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning","volume":"151","author":"Sills","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Hong, T., Liu, X., Deng, J., Li, H., Sun, M., Pan, D., Zhao, Y., Cai, Z., Zhao, J., and Yu, L. (2023, December 04). The Scoring Model to Predict ICU Stay and Mortality After Emergency Admissions in Atrial Fibrillation: A Retrospective Study of 30,206 Patients. Available online: https:\/\/www.researchsquare.com\/article\/rs-3903182\/v1.","DOI":"10.21203\/rs.3.rs-3903182\/v1"},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"e48862","DOI":"10.2196\/48862","article-title":"Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study","volume":"12","author":"Lin","year":"2024","journal-title":"JMIR Med. Inform."},{"key":"ref_201","first-page":"100136","article-title":"Developing a decision model to early predict ICU admission for COVID-19 patients: A machine learning approach","volume":"9","author":"Ahmed","year":"2024","journal-title":"Intell. Med."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s10729-023-09660-5","article-title":"Prediction of hospitalization and waiting time within 24 hours of emergency department patients with un-structured text data","volume":"27","author":"Seo","year":"2024","journal-title":"Health Care Manag. Sci."},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Famiglini, L., Campagner, A., Carobene, A., and Cabitza, F. (2022). A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients. Med. Biol. Eng. Comput., 1\u201313.","DOI":"10.1007\/s11517-022-02543-x"},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/s41746-021-00456-x","article-title":"Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19","volume":"4","author":"Subudhi","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_205","doi-asserted-by":"crossref","unstructured":"Chen, T.-Y., Huang, T.-Y., and Chang, Y.-C. (2024). Using a clinical narrative-aware pre-trained language model for predicting emergency department patient disposition and unscheduled return visits. J. Biomed. Inform., 155.","DOI":"10.1016\/j.jbi.2024.104657"},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"104496","DOI":"10.1016\/j.ijmedinf.2021.104496","article-title":"Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope?","volume":"152","author":"Raven","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_207","doi-asserted-by":"crossref","unstructured":"Choi, D.H., Lee, H., Joo, H., Kong, H.-J., Lee, S.B., Kim, S., Shin, S.D., and Kim, K.H. (2024). Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case\u2013Control Matched Analysis. Diagnostics, 14.","DOI":"10.3390\/diagnostics14080816"},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.annemergmed.2021.02.029","article-title":"Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units","volume":"78","author":"Fenn","year":"2021","journal-title":"Ann. Emerg. Med."},{"key":"ref_209","unstructured":"World Health Organization (1978). International Classification of Diseases: [9th] Ninth Revision, Basic Tabulation List with Alphabetic Index, World Health Organization."},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Bender, D., and Sartipi, K. (2013, January 20\u201322). HL7 FHIR: An Agile and RESTful approach to healthcare information exchange. Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal.","DOI":"10.1109\/CBMS.2013.6627810"},{"key":"ref_211","unstructured":"European Commission (2024, December 13). Proposal for a Regulation of the European Parliament and of the Council on the European Health Data Space. COM(2022) 197 Final. 2022 May 3., Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=CELEX%3A52022PC0197."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1093\/aje\/kwj063","article-title":"The Inconsistency of \u201cOptimal\u201d Cutpoints Obtained using Two Criteria based on the Receiver Operating Characteristic Curve","volume":"163","author":"Perkins","year":"2006","journal-title":"Am. J. Epidemiol."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"3762651","DOI":"10.1155\/2017\/3762651","article-title":"Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach","volume":"2017","author":"Unal","year":"2017","journal-title":"Comput. Math. Methods Med."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"ref_215","first-page":"458","article-title":"Estimation of the Youden Index and its associated cutoff point","volume":"47","author":"Fluss","year":"2005","journal-title":"Biom. J. J. Math. Methods Biosci."},{"key":"ref_216","unstructured":"European Parliament and Council of the European Union (2024, December 13). Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 Laying Down Harmonised Rules on Artificial Intelligence and Amending Regulations (EC) No 300\/2008, (EU) No 167\/2013, (EU) No 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828 (Artificial Intelligence Act) (Text with EEA relevance). 13 June 2024. Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=CELEX%3A32024R1689."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1377\/hlthaff.2014.0048","article-title":"The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care","volume":"33","author":"Cohen","year":"2014","journal-title":"Health Aff."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.3390\/make5030053","article-title":"Artificial intel-ligence ethics and challenges in healthcare applications: A comprehensive review in the context of the European GDPR mandate","volume":"5","author":"Amini","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_219","unstructured":"(2023, December 04). European Commission Ethics Guidelines for Trustworthy AI. Available online: https:\/\/digital-strategy.ec.europa.eu\/en\/library\/ethics-guidelines-trustworthy-ai."},{"key":"ref_220","first-page":"10","article-title":"The eu general data protection regulation (gdpr)","volume":"Volume 10","author":"Voigt","year":"2017","journal-title":"A Practical Guide"},{"key":"ref_221","unstructured":"Prentzas, N., Kakas, A., and Pattichis, C.S. (2023). Explainable AI applications in the Medical Domain: A systematic review. arXiv."},{"key":"ref_222","unstructured":"Lundberg, S., and Lee, S.-I. (2017). SHAP: A Unified Approach to Interpreting Model Predictions. Adv. Neural Inf. Process. Syst., 1\u201310."},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"Kuhn, H., and Tucker, A. (1953). A value for n-person games. Contributions to the Theory of Games II, Princeton University Press.","DOI":"10.1515\/9781400881970"},{"key":"ref_224","first-page":"1","article-title":"All models are wrong, but many are useful: Learning a variable\u2019s importance by studying an entire class of prediction models simultaneously","volume":"20","author":"Fisher","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u2018Why should I trust you?\u2019 Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Biecek, P., and Burzykowski, T. (2021). Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models, CRC Press.","DOI":"10.1201\/9780429027192"},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"100230","DOI":"10.1016\/j.dajour.2023.100230","article-title":"A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends","volume":"7","author":"Saranya","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TSMCA.2005.851270","article-title":"Belief rule-base inference methodology using the evidential reasoning Approach-RIMER","volume":"36","author":"Yang","year":"2006","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_230","first-page":"377","article-title":"Sensitivity analysis as an ingredient of modeling","volume":"15","author":"Saltelli","year":"2000","journal-title":"Stat. Sci."},{"key":"ref_231","unstructured":"Yoon, J., Jordon, J., and van der Schaar, M. (2019, January 6\u20139). INVASE: Instance-wise variable selection using neural networks. Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA."},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.irbm.2021.05.006","article-title":"Towards an Explainable Model for Sepsis Detection Based on Sensitivity Analysis","volume":"43","author":"Chen","year":"2022","journal-title":"IRBM"},{"key":"ref_233","doi-asserted-by":"crossref","unstructured":"Neri, V., Huang, L., and Li, J. (2021). An Explainable Machine Learning Model for Early Prediction of Sepsis Using ICU Data. Infections and Sepsis Development, IntechOpen.","DOI":"10.5772\/intechopen.94701"},{"key":"ref_234","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Kumar, K., Reddy, B.P., Meena, T., and Roy, S. (2023, January 4\u20136). An Explainable AI based Clinical Assistance Model for Iden-tifying Patients with the Onset of Sepsis. Proceedings of the 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI), Bellevue, WA, USA.","DOI":"10.1109\/IRI58017.2023.00059"},{"key":"ref_235","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Bo, L., Wang, L., Xie, Y., Cao, J., Yao, Y., Lu, W., Deng, X., Yang, T., and Bian, J. (2023). Interpretable machine-learning model for real-time, clustered risk factor analysis of sepsis and septic death in critical care. Comput. Methods Programs Biomed., 241.","DOI":"10.1016\/j.cmpb.2023.107772"},{"key":"ref_236","doi-asserted-by":"crossref","unstructured":"Chen, Q., Li, R., Lin, C., Lai, C., Chen, D., Qu, H., Huang, Y., Lu, W., Tang, Y., and Li, L. (2022). Transferability and interpretability of the sepsis prediction models in the intensive care unit. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-02090-3"},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"102036","DOI":"10.1016\/j.artmed.2021.102036","article-title":"DeepAISE\u2014An interpretable and recurrent neural survival model for early prediction of sepsis","volume":"113","author":"Shashikumar","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_238","first-page":"4348","article-title":"An interpretable machine learning model for real-time sepsis pre-diction based on basic physiological indicators","volume":"27","author":"Zhang","year":"2023","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"ref_239","doi-asserted-by":"crossref","unstructured":"Rosnati, M., and Fortuin, V. (2021). MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251248"},{"key":"ref_240","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Bo, L., Xu, Z., Song, Y., Wang, J., Wen, P., Wan, X., Yang, T., Deng, X., and Bian, J. (2021). An explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with ICU readmission. Comput. Methods Programs Biomed., 204.","DOI":"10.1016\/j.cmpb.2021.106040"},{"key":"ref_241","doi-asserted-by":"crossref","unstructured":"Lema\u0144ska-Perek, A., Krzy\u017canowska-Go\u0142\u0105b, D., Kobyli\u0144ska, K., Biecek, P., Skalec, T., Tyszko, M., Gozdzik, W., and Adamik, B. (2022). Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis. Cells, 11.","DOI":"10.3390\/cells11152433"},{"key":"ref_242","unstructured":"Pick, F. (2021). Explainable Machine Learning for Predicting Sepsis Outcome. [Master\u2019s Thesis, Department Computer Science at Swansea University]."},{"key":"ref_243","doi-asserted-by":"crossref","unstructured":"Li, S., Dou, R., Song, X., Lui, K.Y., Xu, J., Guo, Z., Hu, X., Guan, X., and Cai, C. (2023). Developing an Interpretable Machine Learning Model to Predict in-Hospital Mortality in Sepsis Patients: A Retrospective Temporal Validation Study. J. Clin. Med., 12.","DOI":"10.3390\/jcm12030915"},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1007\/s40121-022-00628-6","article-title":"Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study","volume":"11","author":"Hu","year":"2022","journal-title":"Infect. Dis. Ther."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1007\/s40121-022-00671-3","article-title":"Explainable Machine-Learning Model for Prediction of In-Hospital Mortality in Septic Patients Requiring Intensive Care Unit Readmission","volume":"11","author":"Hu","year":"2022","journal-title":"Infect. Dis. Ther."},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"4820464","DOI":"10.1155\/2022\/4820464","article-title":"Interpretable Machine Learning to Optimize Early In-Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study","volume":"2022","author":"Ke","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_247","doi-asserted-by":"crossref","unstructured":"Tasnim, N., and Al Mamun, S. (2023, January 30\u201331). Comparative Performance Analysis of Feature Selection for Mortality Prediction in ICU with Explainable Artificial Intelligence. Proceedings of the 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), Dubai, United Arab Emirates.","DOI":"10.1109\/ECCE57851.2023.10101553"},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"100604","DOI":"10.1109\/ACCESS.2023.3312343","article-title":"A Clinical Decision Support System for Edge\/Cloud ICU Read-mission Model Based on Particle Swarm Optimization, Ensemble Machine Learning, and Explainable Artificial Intelligence","volume":"11","author":"Alabdulhafith","year":"2023","journal-title":"IEEE Access"},{"key":"ref_249","unstructured":"de S\u00e1, A.G.C., Gould, D., Fedyukova, A., Nicholas, M., Dockrell, L., Fletcher, C., Pilcher, D., Capurro, D., Ascher, D.B., and El-Khawas, K. (2023). Explainable Machine Learning for ICU Readmission Prediction. arXiv."},{"key":"ref_250","doi-asserted-by":"crossref","first-page":"110961","DOI":"10.1016\/j.asoc.2023.110961","article-title":"An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout","volume":"149","author":"Zheng","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"6074","DOI":"10.1109\/JBHI.2023.3316750","article-title":"Large AI Models in Health Informatics: Applications, Challenges, and the Future","volume":"27","author":"Qiu","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.3390\/make6030073","article-title":"Evaluation Metrics for Generative Models: An Empirical Study","volume":"6","author":"Betzalel","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_254","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","article-title":"Med-BERT: Pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction","volume":"4","author":"Rasmy","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_255","doi-asserted-by":"crossref","unstructured":"Li, Y., Rao, S., Solares, J.R.A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., and Salimi-Khorshidi, G. (2020). BEHRT: Transformer for Electronic Health Records. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-62922-y"},{"key":"ref_256","doi-asserted-by":"crossref","unstructured":"Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., and Liu, T.-Y. (2022). BioGPT: Generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform., 23.","DOI":"10.1093\/bib\/bbac409"},{"key":"ref_257","unstructured":"Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., and Neal, D. (2023). Towards expert-level medical question answering with large language models. arXiv."},{"key":"ref_258","doi-asserted-by":"crossref","first-page":"e281","DOI":"10.1016\/S2589-7500(24)00025-6","article-title":"Foresight\u2014A generative pretrained transformer for modelling of patient timelines using electronic health records: A retrospective modelling study","volume":"6","author":"Kraljevic","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"ref_259","unstructured":"Team, G., Georgiev, P., Lei, V.I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., and Wang, S. (2024). Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/1\/6\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:25:09Z","timestamp":1759919109000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/1\/6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,8]]},"references-count":259,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["make7010006"],"URL":"https:\/\/doi.org\/10.3390\/make7010006","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,8]]}}}