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

Decision Support for Health Professionals (1191) Artificial Intelligence (1377) Machine Learning (1471) Intensive Care Unit (ICU) (112) Clinical Information and Decision Making (1411)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23888, first published .
Noninvasive Real-Time Mortality Prediction in Intensive Care Units Based on Gradient Boosting Method: Model Development and Validation Study

Noninvasive Real-Time Mortality Prediction in Intensive Care Units Based on Gradient Boosting Method: Model Development and Validation Study

Noninvasive Real-Time Mortality Prediction in Intensive Care Units Based on Gradient Boosting Method: Model Development and Validation Study

Authors of this article:

Huizhen Jiang1 Author Orcid Image ;   Longxiang Su2 Author Orcid Image ;   Hao Wang2 Author Orcid Image ;   Dongkai Li1 Author Orcid Image ;   Congpu Zhao1 Author Orcid Image ;   Na Hong3 Author Orcid Image ;   Yun Long2 Author Orcid Image ;   Weiguo Zhu1 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Hu J, Kang X, Xu F, Huang K, Du B, Weng L. Dynamic prediction of life-threatening events for patients in intensive care unit. BMC Medical Informatics and Decision Making 2022;22(1) View
  2. Werner E, Clark J, Hepburn A, Bhamber R, Ambler M, Bourdeaux C, McWilliams C, Santos-Rodriguez R. Explainable hierarchical clustering for patient subtyping and risk prediction. Experimental Biology and Medicine 2023 View