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

Machine Learning (1971) Clinical Informatics (1257) Clinical Information and Decision Making (1807) Decision Support for Health Professionals (1468)

Published on in Vol 8, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16117, first published .
Predicting Adverse Outcomes for Febrile Patients in the Emergency Department Using Sparse Laboratory Data: Development of a Time Adaptive Model

Predicting Adverse Outcomes for Febrile Patients in the Emergency Department Using Sparse Laboratory Data: Development of a Time Adaptive Model

Predicting Adverse Outcomes for Febrile Patients in the Emergency Department Using Sparse Laboratory Data: Development of a Time Adaptive Model

Authors of this article:

Sungjoo Lee1 Author Orcid Image ;   Sungjun Hong1 Author Orcid Image ;   Won Chul Cha1, 2, 3 Author Orcid Image ;   Kyunga Kim1, 4 Author Orcid Image
Article Authors Cited by (6) Tweetations (1) Metrics

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  2. Ronzio L, Cabitza F, Barbaro A, Banfi G. Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine. Diagnostics 2021;11(2):372 View
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  5. Kuo K, Chang C. A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions. BMC Medical Informatics and Decision Making 2025;25(1) View
  6. Kim S, Nam S, Lee J. Artificial intelligence in emergency department triage: a scoping review on workload reduction and patient safety enhancement. Journal of Korean Biological Nursing Science 2025;27(3):333 View