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

Clinical Informatics (1022) Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Intensive Care Unit (ICU) (112) Electronic Health Records (1053) Tools, Programs and Algorithms (266)

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 (13) Tweetations (6) Metrics

Journals

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  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
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  11. Wang Y, Gao Z, Zhang Y, Lu Z, Sun F. Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study. Internal and Emergency Medicine 2024 View
  12. Rahman M, Islam K, Prithula J, Kumar J, Mahmud M, Alam M, Reaz M, Alqahtani A, Chowdhury M. Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3. BMC Medical Informatics and Decision Making 2024;24(1) View