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

Machine Learning (2029) Decision Support for Health Professionals (1508) Electronic Health Records (1294) Intensive Care Unit (ICU) (156)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35250, first published .
Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study

Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study

Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study

Authors of this article:

Camilo E Valderrama1, 2, 3 Author Orcid Image ;   Daniel J Niven4 Author Orcid Image ;   Henry T Stelfox3, 4 Author Orcid Image ;   Joon Lee1, 2, 3, 5 Author Orcid Image
Article Authors Cited by (4) Tweetations (7) Metrics

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  1. Bunch D, Durant T, Rudolf J. Artificial Intelligence Applications in Clinical Chemistry. Clinics in Laboratory Medicine 2023;43(1):47 View
  2. Devis L, Catry E, Honore P, Mansour A, Lippi G, Mullier F, Closset M. Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review. Annals of Intensive Care 2024;14(1) View
  3. Mo D, Xiong S, Ji T, Zhou Q, Zheng Q. Predicting abnormal C-reactive protein level for improving utilization by deep neural network model. International Journal of Medical Informatics 2025;195:105726 View
  4. Lin Y, K. Mensah I, Doering M, C. Shean R, Spies N. Machine learning-based error detection in the clinical laboratory: a critical review. Critical Reviews in Clinical Laboratory Sciences 2025:1 View