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

Clinical Informatics (908) Machine Learning (1378) Clinical Information and Decision Making (1320) Decision Support for Health Professionals (1133) Intensive Care Unit (ICU) (101) Electronic Health Records (966) Tools, Programs and Algorithms (242)

Published on in Vol 24, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29982, first published .
Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach

Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach

Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach

Authors of this article:

James Yeongjun Park1 Author Orcid Image ;   Tzu-Chun Hsu2 Author Orcid Image ;   Jiun-Ruey Hu3 Author Orcid Image ;   Chun-Yuan Chen4 Author Orcid Image ;   Wan-Ting Hsu5, 6 Author Orcid Image ;   Matthew Lee6 Author Orcid Image ;   Joshua Ho7 Author Orcid Image ;   Chien-Chang Lee2, 7 Author Orcid Image
Article Authors Cited by (9) Tweetations (6) Metrics

Journals

  1. Bowers A, Drake C, Makarkin A, Monzyk R, Maity B, Telle A. Predicting Patient Mortality for Earlier Palliative Care Identification in Medicare Advantage Plans: Features of a Machine Learning Model. JMIR AI 2023;2:e42253 View
  2. Mun E, Cho J. Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ. Tuberculosis and Respiratory Diseases 2023;86(1):23 View
  3. Zhang Y, Xu W, Yang P, Zhang A. Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making 2023;23(1) View
  4. Yang M, Chen H, Hu W, Mischi M, Shan C, Li J, Long X, Liu C. Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study. Journal of Medical Internet Research 2024;26:e50369 View
  5. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3 - Correct DOI):187 View
  6. Lee C, Park J, Hsu W. Bridging expertise with machine learning and automated machine learning in clinical medicine. Annals of the Academy of Medicine, Singapore 2024;53(3 - Correct DOI):129 View
  7. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3):187 View
  8. Lee C, Park J, Hsu W. Bridging expertise with machine learning and automated machine learning in clinical medicine. Annals of the Academy of Medicine, Singapore 2024;53(3):129 View