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

Artificial Intelligence (2129) Validity, Reliability, and Bias (110) Intensive Care Unit (ICU) (160) Clinical Information and Decision Making (1915) Clinical Informatics (1325) Machine Learning (2076) Electronic Health Records (1319)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69293, first published .
Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study

Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study

Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study

Authors of this article:

Zhuo Zheng1 Author Orcid Image ;   Jiawei Luo2 Author Orcid Image ;   Yingchao Zhu1 Author Orcid Image ;   Lei Du1 Author Orcid Image ;   Lan Lan3 Author Orcid Image ;   Xiaobo Zhou4 Author Orcid Image ;   Xiaoyan Yang2 Author Orcid Image ;   Shixin Huang5 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Tian X, Zheng M, Zhuo S, Zheng B. The association between mechanical ventilation and in-hospital mortality in cardiac intensive care units: A propensity score-matched cohort study. Clinics 2025;80:100728 View
  2. Li X, Hu X, Xu H, Yu P, Ju H. Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review. Frontiers in Medicine 2025;12 View

Conference Proceedings

  1. Guo W, Li J. 2025 21st International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). Multimodal Graph Convolutional Networks for Patient Survival Analysis View