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

Formative Evaluation of Digital Health Interventions (2491) Clinical Informatics (1050) Machine Learning (1501) Decision Support for Health Professionals (1210) Clinical Information and Decision Making (1434) Intensive Care Unit (ICU) (114)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42452, first published .
Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

Authors of this article:

Jiang Li1 Author Orcid Image ;   Fengchan Xi2, 3 Author Orcid Image ;   Wenkui Yu3 Author Orcid Image ;   Chuanrui Sun1 Author Orcid Image ;   Xiling Wang1 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Journals

  1. Mahyoub M, Yadav R, Dougherty K, Shukla A. Development and validation of a machine learning model integrated with the clinical workflow for early detection of sepsis. Frontiers in Medicine 2023;10 View
  2. Cao K, Zhang T, Huang J. Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems. Scientific Reports 2024;14(1) View
  3. Li J, Yu Y, Sun Y, Fu Y, Shen W, Cai L, Tan X, Cai Y, Wang N, Lu Y, Wang B. Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes. eLife 2024;13 View
  4. Nguyen T, Poh K, Chong S, Lee J. FedDSS: A data-similarity approach for client selection in horizontal federated learning. International Journal of Medical Informatics 2024;192:105650 View