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

Journals

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  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 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
  24. Salih A, Galazzo I, Gkontra P, Rauseo E, Lee A, Lekadir K, Radeva P, Petersen S, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artificial Intelligence Review 2024;57(9) View
  25. Zhu X, Zhang K, Li X, Su F, Tian J. An interpretable machine learning method for risk stratification of patients with acute coronary syndrome. Heliyon 2024;10(17):e36815 View
  26. Wu S, Li C, Chien T, Chu C. Integrating Structured and Unstructured Data with BERTopic and Machine Learning: A Comprehensive Predictive Model for Mortality in ICU Heart Failure Patients. Applied Sciences 2024;14(17):7546 View
  27. Wu X, Wang Z, Zheng L, Yang Y, Shi W, Wang J, Liu D, Zhang Y. Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery. International Journal of Medical Informatics 2024;192:105609 View
  28. Sun Z, Wang Z, Yun Z, Sun X, Lin J, Zhang X, Wang Q, Duan J, Huang L, Li L, Yao K. Machine learning‐based model for worsening heart failure risk in Chinese chronic heart failure patients. ESC Heart Failure 2024 View
  29. Liu J, Xiao J, Wu H, Ye J, Li Y, Zou B, Li Y. A retrospective cohort study of coagulation function in patients with liver cirrhosis receiving cefoperazone/sulbactam with and without vitamin K1 supplementation. International Journal of Clinical Pharmacy 2024 View
  30. Wang L, Liang D, Huangfu H, Shi X, Liu S, Zhong P, Luo Z, Ke C, Lai Y. Iron Deficiency: Global Trends and Projections from 1990 to 2050. Nutrients 2024;16(20):3434 View
  31. Song Y, Sun Y, Weng Q, Yi L. Using machine learning model for predicting risk of memory decline: A cross sectional study. Heliyon 2024;10(20):e39575 View
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  33. Liu Z, Li J, Zhang Y, Wu D, Huo Y, Yang J, Zhang M, Dong C, Jiang L, Sun R, Zhou R, Li F, Yu X, Zhu D, Guo Y, Chen J. Auxiliary Diagnosis of Children with Attention-Deficit/Hyperactivity Disorder: An Eye-Tracking Study with Novel Digital Biomarkers (Preprint). JMIR mHealth and uHealth 2024 View
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Books/Policy Documents

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