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

Machine Learning (2306) Clinical Information and Decision Making (2149) Decision Support for Health Professionals (1692) Artificial Intelligence (2458) Intensive Care Unit (ICU) (170) mHealth for Diagnosis (90)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/74940, first published .
Man in hospital bed with oxygen nasal cannula and IV drip, vital signs monitor nearby.

Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study

Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study

Authors of this article:

Mingwei Zhang1 Author Orcid Image ;   Ming Zhong2 Author Orcid Image ;   Yunzhang Cheng1, 3 Author Orcid Image ;   Tianyi Zhang1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Garcés M, García J, Montoya J, Triana C, Hernández Rincón E. Artificial intelligence-based models for clinical prognosis at the end of life in the ICU. International Journal of Medical Informatics 2026;206:106156 View
  2. Lin H, Huang Z, Liu M, Li X, Guo Y. Advancing precision triage in strangulated small bowel obstruction: from static scores to dynamic multiparametric models. Frontiers in Surgery 2026;13 View

Conference Proceedings

  1. Shimja.M , Priya K, J S, Jyothi D, K S A, K. P R. 2026 6th International Conference on Expert Clouds and Applications (ICOECA). A Hybrid Deep Learning Framework for Early Sepsis Prediction using Multivariate Time-Series ICU Data based on TabNet-GRU Architecture View