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

Artificial Intelligence (2129) Clinical Information and Decision Making (1915) Decision Support for Health Professionals (1539) Electronic Health Records (1319) Machine Learning (2076) Cardiology (224) Secondary Use of Clinical Data for Research and Surveillance (474) Myocardial Infarction (Heart Attack) (56) Cardiac Risk and Cardiac Risk Calculators (102) New Methods (1134)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27798, first published .
Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach

Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach

Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach

Authors of this article:

Chien-Yu Chi1 Author Orcid Image ;   Shuang Ao2 Author Orcid Image ;   Adrian Winkler2 Author Orcid Image ;   Kuan-Chun Fu2 Author Orcid Image ;   Jie Xu2 Author Orcid Image ;   Yi-Lwun Ho3 Author Orcid Image ;   Chien-Hua Huang4 Author Orcid Image ;   Rohollah Soltani2 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

Journals

  1. Tseng T, Su C, Lai F. Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study. JMIR Medical Informatics 2022;10(10):e42429 View
  2. Sun Y, He Z, Ren J, Wu Y. Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning. BMC Anesthesiology 2023;23(1) View
  3. Ding X, Wang Y, Ma W, Peng Y, Huang J, Wang M, Zhu H. Development of early prediction model of in-hospital cardiac arrest based on laboratory parameters. BioMedical Engineering OnLine 2023;22(1) View
  4. Bertl M, Bignoumba N, Ross P, Yahia S, Draheim D. Evaluation of deep learning-based depression detection using medical claims data. Artificial Intelligence in Medicine 2024;147:102745 View
  5. Lin M, Chi H, Chao W. Multitask learning to predict successful weaning in critically ill ventilated patients: A retrospective analysis of the MIMIC-IV database. DIGITAL HEALTH 2024;10 View
  6. Chi C, Moghadas-Dastjerdi H, Winkler A, Ao S, Chen Y, Wang L, Su P, Lin W, Tsai M, Huang C. Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records. Reviews in Cardiovascular Medicine 2023;24(9) View
  7. Wei S, Guo X, He S, Zhang C, Chen Z, Chen J, Huang Y, Zhang F, Liu Q. Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e67871 View
  8. Wu J, He K, Mao R, Shang X, Cambria E. Harnessing the potential of multimodal EHR data: A comprehensive survey of clinical predictive modeling for intelligent healthcare. Information Fusion 2025;123:103283 View
  9. Chen H, Zeng Y, Cai D. Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models. Frontiers in Artificial Intelligence 2025;8 View
  10. Pierce R, Kell A, Eskridge B, Brandt L, Clary K, Craig K. A Machine Learning-Based Clinical Decision Support System to Improve End-of-Life Care. Applied Clinical Informatics 2025;16(05):1637 View

Books/Policy Documents

  1. Suarez B, Ravelo N, Mpanu Mpanu R, Akinyemiju I, Goldsmith B, Sanchez-Covarrubias A, Gheith N, Montgomerie E, Galli J, Villar-Loubet O, Duthely L. Encyclopedia of Information Science and Technology, Sixth Edition. View

Conference Proceedings

  1. Wang X, Yang X, Tang W, Zhang W. 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). Tolerating Missing Values through Submatrix Ensemble for EHR Classification View