Published on in Vol 3 (2024)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/49784, first published
.
Sepsis Prediction at Emergency Department Triage Using Natural Language Processing: Retrospective Cohort Study
Sepsis Prediction at Emergency Department Triage Using Natural Language Processing: Retrospective Cohort Study
Journals
- Rahmati K, Brown S, Bledsoe J, Passey P, Taillac P, Youngquist S, Samore M, Hough C, Peltan I. Validation and comparison of triage-based screening strategies for sepsis. The American Journal of Emergency Medicine 2024;85:140 View
- Stylianides C, Nicolaou A, Sulaiman W, Alexandropoulou C, Panagiotopoulos I, Karathanasopoulou K, Dimitrakopoulos G, Kleanthous S, Politi E, Ntalaperas D, Papageorgiou X, Garcia F, Antoniou Z, Ioannides N, Palazis L, Vavlitou A, Pattichis M, Pattichis C, Panayides A. AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview. Machine Learning and Knowledge Extraction 2025;7(1):6 View
- Wu H, Hung C, Chen Y, Huang C, Lee J, Hwang S, Ho Y. Using telecommunication dialogue and nursing documentation to predict the risk of emergency room visit in a web-based telehealth programme. European Heart Journal - Digital Health 2025 View
- Al-Juhani A, Desoky R, Iskander Z, Alshehri K, Alshehri A, Almuhaimid A, Alharbi N, Mominah R, Al-humoud F, Desoky A. Advances in Data-Driven Early Warning Systems for Sepsis Recognition and Intervention in Emergency Care: A Systematic Review of Diagnostic Performance and Clinical Outcomes. Cureus 2025 View