This paper is in the following e-collection/theme issue:

Artificial Intelligence (1214) Heart Failure Self-Management (139) Clinical Information and Decision Making (1339) Machine Learning (1400) Decision Support for Health Professionals (1145) Intensive Care Unit (ICU) (102) Clinical Informatics (918) Secondary Use of Clinical Data for Research and Surveillance (325)

Published on in Vol 24, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38082, first published .
Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

Authors of this article:

Jili Li1 Author Orcid Image ;   Siru Liu2 Author Orcid Image ;   Yundi Hu3 Author Orcid Image ;   Lingfeng Zhu4 Author Orcid Image ;   Yujia Mao1 Author Orcid Image ;   Jialin Liu5 Author Orcid Image
Article Authors Cited by (28) Tweetations (6) Metrics

Journals

  1. Liu S, Schlesinger J, McCoy A, Reese T, Steitz B, Russo E, Koh B, Wright A. New onset delirium prediction using machine learning and long short-term memory (LSTM) in electronic health record. Journal of the American Medical Informatics Association 2022;30(1):120 View
  2. Hu X, Yang Z, Ma Y, Wang M, Liu W, Qu G, Zhong C. Development and validation of a machine learning-based predictive model for secondary post-tonsillectomy hemorrhage. Frontiers in Surgery 2023;10 View
  3. Chen Z, Li T, Guo S, Zeng D, Wang K. Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure. Frontiers in Cardiovascular Medicine 2023;10 View
  4. Tian P, Liang L, Zhao X, Huang B, Feng J, Huang L, Huang Y, Zhai M, Zhou Q, Zhang J, Zhang Y. Machine Learning for Mortality Prediction in Patients With Heart Failure With Mildly Reduced Ejection Fraction. Journal of the American Heart Association 2023;12(12) View
  5. Wang L, Duan S, Yan P, Luo X, Zhang N. Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care. Renal Failure 2023;45(1) View
  6. Li X, Shang C, Xu C, Wang Y, Xu J, Zhou Q. Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction. BMC Medical Informatics and Decision Making 2023;23(1) View
  7. Yang X, Qiu H, Wang L, Wang X. Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e44417 View
  8. Dai Y, Ouyang C, Luo G, Cao Y, Peng J, Gao A, Zhou H. Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study. PeerJ 2023;11:e15797 View
  9. Ustin P, Gafarov F, Berdnikov A. Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods. OBM Neurobiology 2023;07(03):1 View
  10. Li L, Ding L, Zhang Z, Zhou L, Zhang Z, Xiong Y, Hu Z, Yao Y. Development and Validation of Machine Learning–Based Models to Predict In-Hospital Mortality in Life-Threatening Ventricular Arrhythmias: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e47664 View
  11. Jia T, Xu K, Bai Y, Lv M, Shan L, Li W, Zhang X, Li Z, Wang Z, Zhao X, Li M, Zhang Y. Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study. BMC Medical Informatics and Decision Making 2023;23(1) View
  12. Sutradhar A, Al Rafi M, Shamrat F, Ghosh P, Das S, Islam M, Ahmed K, Zhou X, Azad A, Alyami S, Moni M. BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients. Scientific Reports 2023;13(1) View
  13. Cai D, Chen Q, Mu X, Xiao T, Gu Q, Wang Y, Ji Y, Sun L, Wei J, Wang Q. Development and validation of a novel combinatorial nomogram model to predict in-hospital deaths in heart failure patients. BMC Cardiovascular Disorders 2024;24(1) View
  14. Li X, Wang Z, Zhao W, Shi R, Zhu Y, Pan H, Wang D. Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease. Renal Failure 2024;46(1) View
  15. Villanueva Solórzano J, Esponda Prado J, Tamborell Rivera A. Enfermedades crónicas no transmisibles como factor de riesgo para mortalidad en cuidados intensivos. Acta Médica Grupo Ángeles 2024;22(1):22 View
  16. Samadi M, Guzman-Maldonado J, Nikulina K, Mirzaieazar H, Sharafutdinov K, Fritsch S, Schuppert A. A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals. Scientific Reports 2024;14(1) View
  17. Strangio A, Leo I, Sabatino J, Brida M, Siracusa C, Carabetta N, Zaffino P, Critelli C, Laschera A, Spadea M, Torella D, Sabouret P, De Rosa S. Is artificial intelligence the new kid on the block? Sustainable applications in cardiology. Vessel Plus 2024 View
  18. Feng S, Wang S, Liu C, Wu S, Zhang B, Lu C, Huang C, Chen T, Zhou C, Zhu J, Chen J, Xue J, Wei W, Zhan X. Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study. Scientific Reports 2024;14(1) View
  19. Gao Z, Liu X, Kang Y, Hu P, Zhang X, Yan W, Yan M, Yu P, Zhang Q, Xiao W, Zhang Z. Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model. Journal of Medical Internet Research 2024;26:e54363 View
  20. Xie P, Wang H, Xiao J, Xu F, Liu J, Chen Z, Zhao W, Hou S, Wu D, Ma Y, Xiao J. Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study. Journal of Medical Internet Research 2024;26:e49848 View
  21. Cao S, Hu Y. Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index. Nutrition & Metabolism 2024;21(1) View
  22. Saqib M, Perswani P, Muneem A, Mumtaz H, Neha F, Ali S, Tabassum S. Machine learning in heart failure diagnosis, prediction, and prognosis: review. Annals of Medicine & Surgery 2024;86(6):3615 View
  23. Li Y, Cao Y, Wang M, Wang L, Wu Y, Fang Y, Zhao Y, Fan Y, Liu X, Liang H, Yang M, Yuan R, Zhou F, Zhang Z, Kang H. Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data. Antimicrobial Resistance & Infection Control 2024;13(1) View

Books/Policy Documents

  1. Herdian C, Widianto S, Ginting J, Geasela Y, Sutrisno J. Engineering Applications of Artificial Intelligence. View
  2. Hu J, Mo C. Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. View