Accessibility settings

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

Artificial Intelligence (4901) Clinical Informatics (2343) Clinical Information and Decision Making (3816) Electronic Health Records (1759) Machine Learning (3281) Intensive Care Unit (ICU) (192) Validity, Reliability, and Bias (146)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69293, first published .
Doctors review brain scan data with patient in hospital bed

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 (19) 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
  3. Wang Y, Zhai S, Wu C, Chen B, Zhang W, Li J, Cheng X, Xiao L. Dynamic ensemble deep learning with multi-source data for robust influenza forecasting in Yangzhou. BMC Public Health 2025;26(1) View
  4. Xu X, Chang P, Liu G, Feng J, Wen J, Chen X, Zhang G. Self-supervised pretraining-based prediction of mortality in patients with traumatic brain injury complicated by hemorrhagic shock model development. Biomedical Signal Processing and Control 2026;116:109471 View
  5. Li L, Jiao M, Tian S, Pei H, Yang X, Bian Y, Zhou M. Applications and challenges of exhaled volatile organic compounds in critically ill patients. Journal of Breath Research 2026;20(1):014002 View
  6. Gautam M, Choudhary A, Agrawal D. Machine Learning Applications in Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury: Enhancing Clinical Decision-Making and Patient Outcomes. Indian Journal of Neurotrauma 2026;23(02):109 View
  7. Von Rekowski C, Fonseca T, Araújo R, Calado C, Bento L, Pinto I. Blood biomarker trajectories in ICU-directed prediction models – A scoping review. EPMA Journal 2026;17(2):427 View
  8. Zhu J, Xie Z, Liu J, Li C. Development and external validation of an ICU mortality prediction model using routinely collected clinical data. Medicine 2026;105(22):e49009 View
  9. Zong L, Bi Q, Liu Y, Lu S, Li C, Wei Z, Ba T, Kong L, Zhang F. Dynamic prognostic prediction in sepsis using longitudinal blood gas trajectories: development and external validation. Frontiers in Medicine 2026;13 View
  10. Song T, Zhao J, Jiang B, Wang X, Li Z, Pang Q. Prediction models for mortality in patients with acute on chronic liver failure: systematic review and critical appraisal. Frontiers in Medicine 2026;13 View
  11. Xu F, Li J, Zhao W, Ding S, Cao J. Dynamic prognostic nutritional index and 28-day mortality in sepsis: a joint modeling analysis. Nutrition & Metabolism 2026;23(1) View
  12. Jafari Z, Zamanifar A, Farhadi A. TA-RNN-Medical-Hybrid: A Time-Aware and Interpretable Framework for Mortality Risk Prediction. IEEE Access 2026;14:108804 View
  13. Hernandez-Diaz C, Vazquez B, Fuentes-Pineda G. Deep learning for multivariate time series analysis in electronic health records: a scoping review. Evolutionary Intelligence 2026;19(5) View
  14. Liu Y, Liu Y, Zhao Y, Luo Y, Hao X. From static snapshots to longitudinal trajectories: artificial intelligence in women’s reproductive and ovarian health. Frontiers in Endocrinology 2026;17 View
  15. Weisman Raymond M, Shahar Y. Predicting Recurring Treatment Events Within Multiple Future Time Windows. Big Data and Cognitive Computing 2026;10(8):281 View
  16. Fathi M, Markazi Moghadam H, Fathi M, Hajiesmaeili M, Nooraei N, Alamdari N, Jame S, Khataee F, Moghaddam N. Early Detection of Clinical Deterioration in ICU Patients With Respiratory Failure Receiving Vasoactive Support: A Machine Learning Approach to Identifying High‐Risk Individuals. Health Science Reports 2026;9(9) View
  17. Li J, Fang X, Yu P, Jiang L. Beyond static risk scores: dynamic world models simulating patient-specific trajectories to inform preoperative risk mitigation strategies. Frontiers in Medicine 2026;13 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
  2. Zheng F, Wu Y, Mascolo C, Dang T. ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Rethinking Large Language Models for Irregular Time Series Classification in Critical Care View