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

Foundations of AI (57) Artificial Intelligence (2034) AI Data (18)

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

Sepsis Prediction at Emergency Department Triage Using Natural Language Processing: Retrospective Cohort Study

Authors of this article:

Felix Brann1 Author Orcid Image ;   Nicholas William Sterling1 Author Orcid Image ;   Stephanie O Frisch1 Author Orcid Image ;   Justin D Schrager1, 2 Author Orcid Image
Article Authors Cited by (6) Tweetations (2) Metrics

Journals

  1. 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
  2. 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
  3. 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;6(5):1036 View
  4. 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
  5. da Silva R, Pazin-Filho A. The incremental value of unstructured data via natural language processing in machine learning-based COVID-19 mortality prediction: a comparative study. BMC Medical Informatics and Decision Making 2025;25(1) View

Conference Proceedings

  1. J A, Mohan M, Parthasarathy S, Jayaraman V, Nikhat H. 2025 International Conference on Sensors and Related Networks (SENNET) Special Focus on Digital Healthcare(64220). SEP-XTree: An Explainable AI Model for Early Sepsis Detection View