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

Machine Learning (1400) Decision Support for Health Professionals (1145) Electronic Health Records (971) Intensive Care Unit (ICU) (102)

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 (2) Tweetations (7) Metrics

Journals

  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