{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:08:19Z","timestamp":1790244499727,"version":"4.1.0"},"reference-count":55,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mozarc Medical\u00ae company, Mirandola","award":["38-033-20-DOT1420214-3771"],"award-info":[{"award-number":["38-033-20-DOT1420214-3771"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Hemodialysis is a treatment for patients with kidney disease, yet it remains risk-prone due to potential technical or physiological instabilities. Dialysis machines rely on reactive safety mechanisms that trigger alarms only when predefined thresholds are reached. This often alerts clinical staff only after a complication has already occurred, contributing to potential delays in interventions. This study aims to develop a proactive prognostic framework capable of anticipating system instabilities before they transform into clinical adverse events. Methods: This work proposes a predictive approach for the proactive detection of patient- or process-related alarm conditions using ten machine learning models and one deep learning architecture. A dataset consisting of over two million records sampled at 0.5 s intervals from 95 dialysis sessions was assembled by merging machine logs of sensor readings and the binary machine status (alarm\/normal). Results: Among the models tested, Random Forest emerged as the most robust predictive tool, achieving an F1 score higher than 0.85 for predicting alarms up to one minute in advance, with precision (~0.83), recall (~0.87), specificity (~0.99), AUROC (~0.99), and AUPRC (~0.94) values indicating strong performance, and stable performance across validation and test sets. A progressive decrease in performance was observed for longer prediction time windows. Conclusions: These findings demonstrate the feasibility of predicting dialysis-machine-generated alarms in advance using machine learning and deep learning models. Providing advance warning could give clinicians the opportunity to anticipate impending alarm conditions and, where appropriate, implement timely corrective actions, potentially improving treatment management and the operational reliability of renal replacement therapies.<\/jats:p>","DOI":"10.3390\/diagnostics16193095","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:07:54Z","timestamp":1790240874000},"page":"3095","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Proactive Prediction of Alarms in Hemodialysis Machines: A Comparative Evaluation of AI Models for Clinical Prognostic Support"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6786-1764","authenticated-orcid":false,"given":"Alessia","family":"Nicosia","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e0 degli Studi di Palermo, Viale delle Scienze Ed. 6, 90128 Palermo, PA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3065-3291","authenticated-orcid":false,"given":"Nunzio","family":"Cancilla","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e0 degli Studi di Palermo, Viale delle Scienze Ed. 6, 90128 Palermo, PA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4453-5352","authenticated-orcid":false,"given":"Marco","family":"Siino","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universit\u00e0 di Catania, Via Santa Sofia 64, 95123 Catania, CA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Michele","family":"Passerini","sequence":"additional","affiliation":[{"name":"Mozarc Medical<sup>\u00ae<\/sup>, Via Camurana 1, 41037 Mirandola, MO, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Francesca","family":"Sau","sequence":"additional","affiliation":[{"name":"Mozarc Medical<sup>\u00ae<\/sup>, Via Camurana 1, 41037 Mirandola, MO, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Ilenia","family":"Tinnirello","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e0 degli Studi di Palermo, Viale delle Scienze Ed. 6, 90128 Palermo, PA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0570-195X","authenticated-orcid":false,"given":"Andrea","family":"Cipollina","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e0 degli Studi di Palermo, Viale delle Scienze Ed. 6, 90128 Palermo, PA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-981-13-8871-2_1","article-title":"Prevalence and Disease Burden of Chronic Kidney Disease","volume":"1165","author":"Lv","year":"2019","journal-title":"Adv. Exp. Med. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ronco, C., and Cruz, D.N. (2008). Hemodialysis: From Basic Research to Clinical Trials, Karger Medical and Scientific Publishers.","DOI":"10.1159\/isbn.978-3-8055-8567-5"},{"key":"ref_3","first-page":"11","article-title":"Mathematical Modelling of Hollow-Fiber Haemodialysis Modules","volume":"10","author":"Cancilla","year":"2025","journal-title":"Phys. Sci. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1115\/1.1590355","article-title":"Effect of Flow Baffles on the Dialysate Flow Distribution of Hollow-Fiber Hemodialyzers: A Nonintrusive Experimental Study Using MRI","volume":"125","author":"Poh","year":"2003","journal-title":"J. Biomech. Eng."},{"key":"ref_5","unstructured":"Murdeshwar, H.N., and Anjum, F. (2024). Hemodialysis. StatPearls, StatPearls Publishing."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dong, Z., Fuentes, L.R., Rao, S., and Kotanko, P. (2024). Closed Loop Ultrafiltration Feedback Control in Hemodialysis: A Narrative Review. Toxins, 16.","DOI":"10.3390\/toxins16080351"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"020067","DOI":"10.1063\/5.0229243","article-title":"Hemodialysis Dialysate Temperature Control System","volume":"3161","author":"Sooriamoorthy","year":"2024","journal-title":"AIP Conf. Proc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s44163-024-00118-3","article-title":"AI: The Future of Humanity","volume":"4","author":"Rawas","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_9","unstructured":"Arai, K., and Lorenz, P. (2026). Radiology\u2013Radiomics Fusion-Based Deep Learning Framework for Malignant Renal Cell Carcinoma Subtype Classification. Proceedings of the Intelligent Computing, Springer Nature."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ren, Z., Zhang, M., Wang, P., Chen, K., Wang, J., Wu, L., Hong, Y., Qu, Y., Luo, Q., and Cai, K. (2025). Research on the Development of an Intelligent Prediction Model for Blood Pressure Variability during Hemodialysis. BMC Nephrol., 26.","DOI":"10.1186\/s12882-025-03959-x"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, Z., Hao, S., Shi, S., Li, L., and Tao, Z. (2025). An Explainable Machine Learning Model for Early Warning of Hypertensive and Hypotensive Anomalies in Maintenance Hemodialysis Patients. BMC Nephrol., 26, Correction in BMC Nephrol.\u00a02025, 26, 515.","DOI":"10.1186\/s12882-025-04447-y"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, R., Shu, X., and Zhang, H. (2024). Classification of Arteriovenous Fistula Sounds Using a Convolutional Block Attention Module and Long Short-Term Memory Neural Network. Front. Physiol., 15.","DOI":"10.3389\/fphys.2024.1397317"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1007\/s00467-018-4015-2","article-title":"Artificial Intelligence Outperforms Experienced Nephrologists to Assess Dry Weight in Pediatric Patients on Chronic Hemodialysis","volume":"33","author":"Niel","year":"2018","journal-title":"Pediatr. Nephrol. Berl. Ger."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ota, K., Nishiura, Y., Ishihara, S., Adachi, H., Yamamoto, T., and Hamano, T. (2020). Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning. Sensors, 20.","DOI":"10.3390\/s20174852"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1177\/11297298231156632","article-title":"Utility of Machine Learning (ML)Analytics on Real Time Risk Stratification and Re-Intervention Risk Prediction on AV Access Outcomes and Cost","volume":"25","author":"Balamuthusamy","year":"2023","journal-title":"J. Vasc. Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Motofelea, A.C., Mihaescu, A., Olariu, N., Marc, L., Chisavu, L., Pop, G.N., Crintea, A., Jura, A.M.C., Ivan, V.M., and Apostol, A. (2025). Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review. Appl. Sci., 15.","DOI":"10.3390\/app15105776"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10434","DOI":"10.1038\/s41598-025-95287-1","article-title":"Development of an Early Prediction Model for Vomiting during Hemodialysis Using LASSO Regression and Boruta Feature Selection","volume":"15","author":"Chen","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s41100-025-00632-9","article-title":"Dialysis Machine Alarm Recognition Based on Convolutional Neural Network","volume":"11","author":"Xie","year":"2025","journal-title":"Ren. Replace. Ther."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e1279","DOI":"10.7717\/peerj-cs.1279","article-title":"Predicting Medical Device Failure: A Promise to Reduce Healthcare Facilities Cost through Smart Healthcare Management","volume":"9","author":"Rahman","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chromik, J., Klopfenstein, S.A.I., Pfitzner, B., Sinno, Z.-C., Arnrich, B., Balzer, F., and Poncette, A.-S. (2022). Computational Approaches to Alleviate Alarm Fatigue in Intensive Care Medicine: A Systematic Literature Review. Front. Digit. Health, 4.","DOI":"10.3389\/fdgth.2022.843747"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1038\/s41591-020-0789-4","article-title":"Early Prediction of Circulatory Failure in the Intensive Care Unit Using Machine Learning","volume":"26","author":"Hyland","year":"2020","journal-title":"Nat. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108335","DOI":"10.1016\/j.cmpb.2024.108335","article-title":"Continuous Prediction and Clinical Alarm Management of Late-Onset Sepsis in Preterm Infants Using Vital Signs from a Patient Monitor","volume":"255","author":"Yang","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jarm, T., \u0160merc, R., and Mahni\u010d-Kalamiza, S. (2024). Alarms Early Detection in Dialytic Therapies via Machine Learning Models. Proceedings of the 9th European Medical and Biological Engineering Conference, Springer Nature.","DOI":"10.1007\/978-3-031-61628-0"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"210","DOI":"10.4103\/IJPSYM.IJPSYM_193_19","article-title":"The P Value and Statistical Significance: Misunderstandings, Explanations, Challenges, and Alternatives","volume":"41","author":"Andrade","year":"2019","journal-title":"Indian J. Psychol. Med."},{"key":"ref_25","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1214\/09-SS054","article-title":"A Survey of Cross-Validation Procedures for Model Selection","volume":"4","author":"Arlot","year":"2010","journal-title":"Stat. Surv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, Y., Koprinska, I., and Rana, M. (2021, January 18\u201322). Temporal Convolutional Attention Neural Networks for Time Series Forecasting. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9534351"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1002\/sam.11583","article-title":"Optimal Ratio for Data Splitting","volume":"15","author":"Joseph","year":"2022","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"ref_29","first-page":"1","article-title":"Feature Selection: A Data Perspective","volume":"50","author":"Li","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Abid-Althaqafi, N.R., and Alsalamah, H.A. (2024). The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning. Electronics, 13.","DOI":"10.3390\/electronics13010205"},{"key":"ref_31","unstructured":"(2026, September 15). Mutual_Info_Classif, Scikit-Learn Documentation, Scikit-Learn Developers. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.mutual_info_classif.html."},{"key":"ref_32","first-page":"36","article-title":"A Comparison of the Pearson, Spearman Rank and Kendall Tau Correlation Coefficients Using Quantitative Variables","volume":"20","author":"Shiekh","year":"2022","journal-title":"Asian J. Probab. Stat."},{"key":"ref_33","unstructured":"(2026, September 15). Supervised Learning, Scikit-Learn Documentation, Scikit-Learn Developers. Available online: https:\/\/scikit-learn.org\/stable\/supervised_learning.html."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest Neighbor Pattern Classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"van der Aalst, W.M.P., Batagelj, V., Ignatov, D.I., Khachay, M., Kuskova, V., Kutuzov, A., Kuznetsov, S.O., Lomazova, I.A., Loukachevitch, N., and Napoli, A. (2019). Histogram-Based Algorithm for Building Gradient Boosting Ensembles of Piecewise Linear Decision Trees. Proceedings of the Analysis of Images, Social Networks and Texts, Springer International Publishing.","DOI":"10.1007\/978-3-030-37334-4"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13\u201317 August 2016, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_40","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1504\/IJDATS.2011.041335","article-title":"Logistic Regression in Data Analysis: An Overview","volume":"3","author":"Maalouf","year":"2011","journal-title":"Int. J. Data. Anal. Tech. Strateg."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge Regression: Biased Estimation for Nonorthogonal Problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0925-2312(91)90023-5","article-title":"Multilayer Perceptrons for Classification and Regression","volume":"2","author":"Murtagh","year":"1991","journal-title":"Neurocomputing"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Auto-Sklearn: Efficient and Robust Automated Machine Learning. Automated Machine Learning, Springer International Publishing. The Springer Series on Challenges in Machine Learning.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref_45","first-page":"1","article-title":"AMLB: An AutoML Benchmark","volume":"25","author":"Gijsbers","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A Systematic Analysis of Performance Measures for Classification Tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_47","first-page":"345","article-title":"A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation","volume":"Volume 3408","author":"Losada","year":"2005","journal-title":"Advances in Information Retrieval"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Nicosia, A., Cancilla, N., Mart\u00edn Guerrero, J.D., Tinnirello, I., and Cipollina, A. (2025). Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases. Bioengineering, 12.","DOI":"10.3390\/bioengineering12101069"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"499","DOI":"10.2147\/TCRM.S126191","article-title":"Patient Characteristics Associated with False Arrhythmia Alarms in Intensive Care","volume":"13","author":"Harris","year":"2017","journal-title":"Ther. Clin. Risk Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s12938-025-01493-3","article-title":"Hemodialysis Equipment Malfunction Incidence and Risk Profiling in Clinical Settings: A Retrospective Single-Center Cohort Analysis","volume":"24","author":"Wang","year":"2025","journal-title":"Biomed. Eng. OnLine"},{"key":"ref_51","first-page":"1761","article-title":"Real-Time Prediction of Intradialytic Hypotension Using Machine Learning and Cloud Computing Infrastructure","volume":"38","author":"Zhang","year":"2023","journal-title":"Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc.\u2014Eur. Ren. Assoc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on Deep Learning with Class Imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"129896","DOI":"10.1016\/j.neucom.2025.129896","article-title":"Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions","volume":"634","author":"Luo","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"197","DOI":"10.2147\/IJNRD.S461028","article-title":"Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery","volume":"17","author":"Omar","year":"2024","journal-title":"Int. J. Nephrol. Renov. Dis."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Nicosia, A., Cancilla, N., Passerini, M., Sau, F., Tinnirello, I., and Cipollina, A. (2025). Performance Comparison of a Neural Network and a Regression Linear Model for Predictive Maintenance in Dialysis Machine Components. Bioengineering, 12.","DOI":"10.3390\/bioengineering12090941"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3095\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:26:01Z","timestamp":1790241961000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":55,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["diagnostics16193095"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16193095","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}