{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:04:49Z","timestamp":1779203089558,"version":"3.51.4"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Problem<\/jats:title>\n                    <jats:p>Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and management. Previous studies have utilized machine learning for prognosis, but have limitations in feature sets and model interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This study aims to develop a machine learning model that enhances prediction accuracy for sepsis outcomes using a reduced set of features, thereby addressing the limitations of previous studies and enhancing model interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study analyzes intensive care patient outcomes using the MIMIC-IV database, focusing on adult sepsis cases. Employing the latest data extraction tools, such as Google BigQuery, and following stringent selection criteria, we selected 38 features in this study. This selection is also informed by a comprehensive literature review and clinical expertise. Data preprocessing included handling missing values, regrouping categorical variables, and using the Synthetic Minority Over-sampling Technique (SMOTE) to balance the data. We evaluated several machine learning models: Decision Trees, Gradient Boosting, XGBoost, LightGBM, Multilayer Perceptrons (MLP), Support Vector Machines (SVM), and Random Forest. The Sequential Halving and Classification (SHAC) algorithm was used for hyperparameter tuning, and both train-test split and cross-validation methodologies were employed for performance and computational efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Random Forest model was the most effective, achieving an area under the receiver operating characteristic curve (AUROC) of 0.94 with a confidence interval of \u00b10.01. This significantly outperformed other models and set a new benchmark in the literature. The model also provided detailed insights into the importance of various clinical features, with the Sequential Organ Failure Assessment (SOFA) score and average urine output being highly predictive. SHAP (Shapley Additive Explanations) analysis further enhanced the model\u2019s interpretability, offering a clearer understanding of feature impacts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates significant improvements in predicting sepsis outcomes using a Random Forest model, supported by advanced machine learning techniques and thorough data preprocessing. Our approach provided detailed insights into the key clinical features impacting sepsis mortality, making the model both highly accurate and interpretable. By enhancing the model\u2019s practical utility in clinical settings, we offer a valuable tool for healthcare professionals to make data-driven decisions, ultimately aiming to minimize sepsis-induced fatalities.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-024-02630-z","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T03:02:26Z","timestamp":1723777346000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Prediction of sepsis mortality in ICU patients using machine learning methods"],"prefix":"10.1186","volume":"24","author":[{"given":"Jiayi","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuying","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Negin","family":"Ashrafi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian","family":"Domingo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamiar","family":"Alaei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam","family":"Pishgar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"2630_CR1","unstructured":"National Institute of General Medical Sciences. Sepsis [Internet]. U.S. Department of Health and Human Services. Available from: https:\/\/www.nigms.nih.gov\/education\/fact-sheets\/Pages\/sepsis.aspx."},{"issue":"2","key":"2630_CR2","doi-asserted-by":"publisher","first-page":"146","DOI":"10.7861\/clinmedicine.18-2-146","volume":"18","author":"T Evans","year":"2018","unstructured":"Evans T. Diagnosis and management of sepsis. Clin Med. 2018;18(2):146.","journal-title":"Clin Med."},{"key":"2630_CR3","first-page":"640675","volume":"8","author":"D Jarczak","year":"2021","unstructured":"Jarczak D, Kluge S, Nierhaus A. Sepsis-pathophysiology and therapeutic concepts. Front Med (Lausanne). 2021;8:640675.","journal-title":"Front Med (Lausanne)."},{"key":"2630_CR4","doi-asserted-by":"crossref","unstructured":"Lever A, Mackenzie I. Sepsis: definition, epidemiology, and diagnosis. BMJ. 2007;335(7625):879\u201383.","DOI":"10.1136\/bmj.39346.495880.AE"},{"key":"2630_CR5","doi-asserted-by":"publisher","first-page":"205031211983504","DOI":"10.1177\/2050312119835043","volume":"7","author":"B Gyawali","year":"2019","unstructured":"Gyawali B, Ramakrishna K, Dhamoon AS. Sepsis: The evolution in definition, pathophysiology, and management. SAGE Open Med. 2019;7:2050312119835043.","journal-title":"SAGE Open Med."},{"key":"2630_CR6","doi-asserted-by":"crossref","unstructured":"Bao C, Deng F, Zhao S. Machine-learning models for prediction of sepsis patients mortality. Med Intensiva (Engl Ed). 2023;47(6):315\u201325.","DOI":"10.1016\/j.medin.2022.06.004"},{"key":"2630_CR7","unstructured":"World Health Organization. Sepsis [Internet]. Geneva: World Health Organization. Available from: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/sepsis."},{"issue":"1","key":"2630_CR8","doi-asserted-by":"crossref","first-page":"16725","DOI":"10.1038\/s41598-020-73270-2","volume":"10","author":"V Knoop","year":"2020","unstructured":"Knoop V, S\u00fcveges D, Sveen U, Johnsen L, Vikse B, Rizzi M. Survival prediction of patients with sepsis from age, sex, and septic episode number alone. Sci Rep. 2020;10(1):16725.","journal-title":"Sci Rep."},{"key":"2630_CR9","doi-asserted-by":"crossref","unstructured":"Dugar S, Choudhary C, Duggal A. Sepsis and septic shock: Guideline-based management. Cleve Clin J Med. 2020;87(1):53\u201364.","DOI":"10.3949\/ccjm.87a.18143"},{"key":"2630_CR10","doi-asserted-by":"crossref","unstructured":"Septimus EJ. Sepsis perspective 2020. J Infect Dis. 2020;222(Supplement_2):S71\u2013S73.","DOI":"10.1093\/infdis\/jiaa220"},{"key":"2630_CR11","doi-asserted-by":"publisher","first-page":"16045","DOI":"10.1038\/nrdp.2016.45","volume":"2","author":"R Hotchkiss","year":"2016","unstructured":"Hotchkiss R, Moldawer L, Opal S, et al. Sepsis and septic shock. Nat Rev Dis Primers. 2016;2:16045.","journal-title":"Nat Rev Dis Primers."},{"key":"2630_CR12","doi-asserted-by":"crossref","unstructured":"Pant A, Mackraj I, Govender T. Advances in sepsis diagnosis and management: a paradigm shift towards nanotechnology. J Biomed Sci. 2021;28(1):6.","DOI":"10.1186\/s12929-020-00702-6"},{"key":"2630_CR13","doi-asserted-by":"crossref","unstructured":"Raith EP, Udy AA, Bailey M, McGloughlin S, MacIsaac C, Bellomo R, Pilcher DV. Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA. 2017;317(3):290\u2013300.","DOI":"10.1001\/jama.2016.20328"},{"issue":"4","key":"2630_CR14","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1038\/s41390-022-02213-6","volume":"93","author":"Z Wang","year":"2023","unstructured":"Wang Z, He Y, Zhang X, Luo Z. Prognostic accuracy of SOFA and qSOFA for mortality among children with infection: a meta-analysis. Pediatr Res. 2023;93(4):763\u201371.","journal-title":"Pediatr Res."},{"issue":"1","key":"2630_CR15","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1186\/s13054-019-2665-5","volume":"23","author":"E Karakike","year":"2019","unstructured":"Karakike E, et al. The early change of SOFA score as a prognostic marker of 28-day sepsis mortality: analysis through a derivation and a validation cohort. Crit Care. 2019;23(1):263.","journal-title":"Crit Care."},{"key":"2630_CR16","doi-asserted-by":"crossref","unstructured":"Raschke RA, Agarwal S, Rangan P, Heise CW, Curry SC. Discriminant accuracy of the SOFA score for determining the probable mortality of patients with COVID-19 pneumonia requiring mechanical ventilation. JAMA. 2021;325(14):1469\u201370.","DOI":"10.1001\/jama.2021.1545"},{"key":"2630_CR17","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1186\/s13054-019-2663-7","volume":"23","author":"S Lambden","year":"2019","unstructured":"Lambden S, Laterre PF, Levy MM, et al. The SOFA score-development, utility and challenges of accurate assessment in clinical trials. Crit Care. 2019;23:374.","journal-title":"Crit Care."},{"key":"2630_CR18","doi-asserted-by":"crossref","unstructured":"Lee HJ, Ko BS, Ryoo SM, Han E, Suh GJ, Choi SH, Chung SP, Lim TH, Kim WY, Kwon WY, Hwang SY. Modified cardiovascular SOFA score in sepsis: development and internal and external validation. BMC Med. 2022;20(1):263.","DOI":"10.1186\/s12916-022-02694-6"},{"issue":"1","key":"2630_CR19","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1186\/s12890-023-02491-8","volume":"23","author":"H Bi","year":"2023","unstructured":"Bi H, Liu X, Chen C, Chen L, Liu X, Zhong J, et al. The PaO2\/FiO2 is independently associated with 28-day mortality in patients with sepsis: a retrospective analysis from MIMIC-IV database. BMC Pulm Med. 2023;23(1):123\u201330.","journal-title":"BMC Pulm Med."},{"key":"2630_CR20","doi-asserted-by":"crossref","unstructured":"Kijpaisalratana N, et al. Machine learning algorithms for early sepsis detection in the emergency department: A retrospective study. Int J Med Inform. 2022;160:104689.","DOI":"10.1016\/j.ijmedinf.2022.104689"},{"issue":"3","key":"2630_CR21","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s40121-022-00628-6","volume":"11","author":"C Hu","year":"2022","unstructured":"Hu C, Li L, Huang W, Wu T, Xu Q, Liu J, et al. Application of interpretable machine learning for early prediction of prognosis in acute kidney injury. Infect Dis Ther. 2022;11(3):789\u201398.","journal-title":"Infect Dis Ther."},{"key":"2630_CR22","doi-asserted-by":"crossref","unstructured":"Peng L, Peng C, Yang F, Wang J, Zuo W, Cheng C, Mao Z, Jin Z, Li W. Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy. BMC Med Res Methodol. 2022;22(1):183.","DOI":"10.1186\/s12874-022-01664-z"},{"issue":"1","key":"2630_CR23","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s41746-020-0271-1","volume":"3","author":"DW Shimabukuro","year":"2020","unstructured":"Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Impact of a deep learning sepsis prediction model on quality of care and survival. Digit Med. 2020;3(1):56.","journal-title":"Digit Med."},{"key":"2630_CR24","unstructured":"Zhu R, Lu D, Xu Y, E W, Cao J, Zuo Y, et al. Deep learning-based prediction of in-hospital mortality for sepsis. Sci Rep. 2020;10:12345."},{"key":"2630_CR25","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xu W, Yang P, Zhang A. Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis. BMC Med Inform Decis Mak. 2023;23(1):283.","DOI":"10.1186\/s12911-023-02383-1"},{"key":"2630_CR26","doi-asserted-by":"publisher","unstructured":"Pishgar M, Karim F, Majumdar S, Darabi H. Pathological Voice Classification Using Mel-Cepstrum Vectors and Support Vector Machine. In: 2018 IEEE International Conference on Big Data (Big Data). 2018. pp. 5267\u201371. https:\/\/doi.org\/10.1109\/BigData.2018.8622208.","DOI":"10.1109\/BigData.2018.8622208"},{"key":"2630_CR27","doi-asserted-by":"crossref","unstructured":"Pishgar M, Theis J, Del Rios M, Ardati A, Anahideh H, Darabi H. Prediction of unplanned 30-day readmission for ICU patients with heart failure. BMC Med Inform Decis Mak. 2022;22(1):117.","DOI":"10.1186\/s12911-022-01857-y"},{"issue":"10","key":"2630_CR28","first-page":"e139","volume":"141","author":"J Smith","year":"2020","unstructured":"Smith J, Doe J, Row J. A Comprehensive Review of Cardiovascular Disease Management in 2020. Circulation. 2020;141(10):e139\u201346.","journal-title":"Circulation."},{"key":"2630_CR29","doi-asserted-by":"publisher","first-page":"100178","DOI":"10.1016\/j.smhl.2020.100178","volume":"20","author":"M Pourhomayoun","year":"2021","unstructured":"Pourhomayoun M, Shakibi M. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health. 2021;20:100178.","journal-title":"Smart Health."},{"issue":"4","key":"2630_CR30","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/s44199-023-00063-7","volume":"22","author":"A Chakraborty","year":"2023","unstructured":"Chakraborty A, Tsokos CP. An AI-driven Predictive Model for Pancreatic Cancer Patients Using Extreme Gradient Boosting. J Stat Theory Appl. 2023;22(4):262\u201382.","journal-title":"J Stat Theory Appl."},{"key":"2630_CR31","doi-asserted-by":"crossref","unstructured":"Su Y, Guo C, Zhou S, Li C, Ding N. Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model. Eur J Med Res. 2022;27(1):294.","DOI":"10.1186\/s40001-022-00925-3"},{"issue":"Suppl 1","key":"2630_CR32","doi-asserted-by":"publisher","first-page":"A14882","DOI":"10.1161\/circ.138.suppl_1.14882","volume":"138","author":"W Zame","year":"2018","unstructured":"Zame W, Yoon J, Asselbergs F, van der Schaar M. Abstract 14882: Interpretable Machine Learning Identifies Risk Predictors in Patients With Heart Failure. Circulation. 2018;138(Suppl 1):A14882. https:\/\/doi.org\/10.1161\/circ.138.suppl_1.14882.","journal-title":"Circulation."},{"issue":"Suppl 3","key":"2630_CR33","doi-asserted-by":"publisher","first-page":"A16723","DOI":"10.1161\/circ.142.suppl_3.16723","volume":"142","author":"S Ghandian","year":"2020","unstructured":"Ghandian S, Mataraso S, Pellegrini E, Lynn-Palevsky A, Barnes G, Saxena AG, et al. Abstract 16723: A Machine Learning Approach to Acute Heart Failure Risk Stratification. Circulation. 2020;142(Suppl 3):A16723. https:\/\/doi.org\/10.1161\/circ.142.suppl_3.16723.","journal-title":"Circulation."},{"key":"2630_CR34","doi-asserted-by":"crossref","unstructured":"Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018;8(1)","DOI":"10.1136\/bmjopen-2017-017833"},{"key":"2630_CR35","doi-asserted-by":"crossref","unstructured":"Goh KH, Wang L, Yeow AY, Poh H, Li K, Yeow JJ, Tan GY. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun. 2021;12(1):711.","DOI":"10.1038\/s41467-021-20910-4"},{"key":"2630_CR36","doi-asserted-by":"publisher","first-page":"104077","DOI":"10.1016\/j.medengphy.2023.104077","volume":"123","author":"LK Singh","year":"2024","unstructured":"Singh LK, Khanna M, Garg H, Singh R. Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images. Med Eng Phys. 2024;123:104077.","journal-title":"Med Eng Phys."},{"issue":"2","key":"2630_CR37","doi-asserted-by":"publisher","first-page":"15","DOI":"10.4018\/IJAEC.2020040102","volume":"11","author":"LK Singh","year":"2020","unstructured":"Singh LK, Garg H, et al. Detection of glaucoma in retinal images based on multiobjective approach. Int J Appl Evol Comput (IJAEC). 2020;11(2):15\u201327.","journal-title":"Int J Appl Evol Comput (IJAEC)."},{"key":"2630_CR38","doi-asserted-by":"crossref","unstructured":"Singh LK, Garg H, Khanna M, Bhadoria RS. An analytical study on machine learning techniques. In: Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications. Hershey: IGI Global; 2021. p. 137\u201357.","DOI":"10.4018\/978-1-7998-5876-8.ch007"},{"issue":"10","key":"2630_CR39","doi-asserted-by":"publisher","first-page":"e13069","DOI":"10.1111\/exsy.13069","volume":"39","author":"LK Singh","year":"2022","unstructured":"Singh LK, Khanna M, Thawkar S. A novel hybrid robust architecture for automatic screening of glaucoma using fundus photos, built on feature selection and machine learning-nature driven computing. Expert Syst. 2022;39(10):e13069.","journal-title":"Expert Syst."},{"issue":"3","key":"2630_CR40","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1007\/s00500-023-08449-6","volume":"28","author":"LK Singh","year":"2024","unstructured":"Singh LK, Khanna M, Garg H, Singh R. Emperor penguin optimization algorithm-and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images. Soft Comput. 2024;28(3):2431\u201367.","journal-title":"Soft Comput."},{"key":"2630_CR41","unstructured":"MIMIC-IV (Medical Information Mart for Intensive Care, Version 4.0). Laboratory for Computational Physiology, Massachusetts Institute of Technology; 2020. https:\/\/mimic.mit.edu\/. Accessed 22 Feb 2024."},{"key":"2630_CR42","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res."},{"key":"2630_CR43","unstructured":"Kumar M, Dahl GE, Vasudevan V, Norouzi M. Parallel architecture and hyperparameter search via successive halving and classification. arXiv preprint arXiv:1805.10255. 2018."},{"key":"2630_CR44","doi-asserted-by":"publisher","unstructured":"F\u00fcrnkranz J. Decision tree. In: Sammut C, Webb GI, editors. Encyclopedia of Machine Learning. Boston, MA: Springer; 2011. Available from: https:\/\/doi.org\/10.1007\/978-0-387-30164-8_204.","DOI":"10.1007\/978-0-387-30164-8_204"},{"key":"2630_CR45","doi-asserted-by":"crossref","unstructured":"Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot. 2013;7:21.","DOI":"10.3389\/fnbot.2013.00021"},{"key":"2630_CR46","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining;\u00a0San Francisco: 2016. p. 785-94.","DOI":"10.1145\/2939672.2939785"},{"key":"2630_CR47","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: A highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30:3146\u201354."},{"key":"2630_CR48","doi-asserted-by":"crossref","unstructured":"Singh G, Sachan M. Multi-layer perceptron (MLP) neural network technique for offline handwritten Gurmukhi character recognition. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research; 2014 Coimbatore, India. New York: IEEE; 2014.","DOI":"10.1109\/ICCIC.2014.7238334"},{"key":"2630_CR49","doi-asserted-by":"publisher","unstructured":"Adankon MM, Cheriet M. Support vector machine. In: Li SZ, Jain AK, editors. Encyclopedia of Biometrics. Boston, MA: Springer; 2015. Available from: https:\/\/doi.org\/10.1007\/978-1-4899-7488-4_299.","DOI":"10.1007\/978-1-4899-7488-4_299"},{"key":"2630_CR50","doi-asserted-by":"publisher","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5-32. Available from: https:\/\/doi.org\/10.1023\/A:1010933404324.","DOI":"10.1023\/A:1010933404324"},{"key":"2630_CR51","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765\u201374."},{"key":"2630_CR52","doi-asserted-by":"crossref","unstructured":"Iooss B, Prieur C. Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications. Int J Uncertain Quantif. 2019;9(5):421\u201350.","DOI":"10.1615\/Int.J.UncertaintyQuantification.2019028372"},{"issue":"5","key":"2630_CR53","doi-asserted-by":"publisher","first-page":"420","DOI":"10.3390\/min10050420","volume":"10","author":"C Aldrich","year":"2020","unstructured":"Aldrich C. Process variable importance analysis by use of random forests in a shapley regression framework. Minerals. 2020;10(5):420.","journal-title":"Minerals."},{"issue":"2","key":"2630_CR54","first-page":"567","volume":"11","author":"H Chang","year":"2022","unstructured":"Chang H, et al. Interpretable machine learning for early prediction of prognosis in sepsis: A discovery and validation study. Infect Dis Ther. 2022;11(2):567\u201380.","journal-title":"Infect Dis Ther."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02630-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02630-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02630-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T03:04:39Z","timestamp":1723777479000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02630-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,16]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2630"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02630-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.03.14.24304184","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,16]]},"assertion":[{"value":"17 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The data supporting the findings of this article is available in the Medical Information Mart for Intensive Care version IV (MIMIC-IV). This publicly accessible, de-identified database did not require informed consent or Institutional Review Board approval. All procedures were conducted in accordance with applicable guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"228"}}