Accessibility settings

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

AI Language Models in Health Care (836) Clinical Informatics (2392) Clinical Information and Decision Making (3894) Artificial Intelligence (5027)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/80969, first published .
Man with oxygen nasal cannula in hospital bed, monitored by medical equipment.

Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study

Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study

Authors of this article:

Yan Zhang1 Author Orcid Image ;   Xia Ren2 Author Orcid Image ;   Luojie Liu2 Author Orcid Image ;   Junjie Zha1 Author Orcid Image ;   Yijie Gu1 Author Orcid Image ;   Hongwei Ye1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Li Y, Xin Q, Lu Y, Yu X, Gu C. Explainable machine learning for predicting venous thromboembolism in septic shock patients. Frontiers in Immunology 2026;17 View
  2. Ma L, Yu X, Xin Q. Interpretable machine learning models to predict venous thromboembolism in sepsis patients with type 2 diabetes mellitus. Frontiers in Endocrinology 2026;17 View
  3. Han Y, Shi Y, Liu L, Ding B, Li M, Xia A, Liu J, Song J, Xie A, Miao D, Zhou H. Investigating risk factors and developing a predictive model for venous thromboembolism in patients with sepsis. Frontiers in Medicine 2026;13 View