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

Formative Evaluation of Digital Health Interventions (2768) Clinical Informatics (1129) Machine Learning (1683) Decision Support for Health Professionals (1321) Clinical Information and Decision Making (1601) Intensive Care Unit (ICU) (133)

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 (11) 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
  5. Szumilas M. Biosignal-Based Machine Learning Predictors of Sepsis: A Mini-Review. Acta Physica Polonica A 2024;146(4):388 View
  6. Sun B, Lei M, Wang L, Wang X, Li X, Mao Z, Kang H, Liu H, Sun S, Zhou F. Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study. International Journal of Surgery 2025;111(1):467 View
  7. Hamid R, Zahid I, Albahri A, Albahri O, Alamoodi A, Alzubaidi L, Sharaf I, Joudar S, Gu Y, Al‐qaysi Z. Fuzzy Decision‐Making Framework for Evaluating Hybrid Detection Models of Trauma Patients. Expert Systems 2025;42(3) View
  8. Turan E, Baydemir A, Balıtatlı A, Şahin A. Assessing the accuracy of ChatGPT in interpreting blood gas analysis results ChatGPT-4 in blood gas analysis. Journal of Clinical Anesthesia 2025;102:111787 View
  9. Rahman M, Chowdhury M, Shorfuzzaman M, Karim L, Shafiullah M, Azzedin F. Enhancing Septic Shock Detection through Interpretable Machine Learning. Computer Modeling in Engineering & Sciences 2024;141(3):2501 View
  10. Gou Y, Lv B, Zhang J, Li S, Hei X, Liu J, Li L, Yang J, Feng K. Identifying early predictive and diagnostic biomarkers and exploring metabolic pathways for sepsis after trauma based on an untargeted metabolomics approach. Scientific Reports 2025;15(1) View