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

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

Books/Policy Documents

  1. Herdian C, Widianto S, Ginting J, Geasela Y, Sutrisno J. Engineering Applications of Artificial Intelligence. View