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Related Experiment Video

Updated: Sep 21, 2025

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Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on

Camilo E Valderrama1,2,3, Daniel J Niven4, Henry T Stelfox3,4

  • 1Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

JMIR Medical Informatics
|June 3, 2022
PubMed
Summary

This study introduces novel machine learning features, conditional entropy and probability, to predict abnormal lab test results in ICUs, significantly improving accuracy and aiding in reducing unnecessary testing.

Keywords:
blood laboratory test redundancyelectronic medical recordsfuzzy modelingintensive care unitmachine learning

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Area of Science:

  • Medical informatics
  • Machine learning in healthcare
  • Clinical laboratory science

Background:

  • Redundant laboratory blood tests in ICUs increase costs and impact patient health.
  • Data-driven approaches can identify low-yield tests, but previous methods haven't integrated conditional entropy or probability into predictive models.
  • Existing methods lack the ability to measure uncertainty in predicting abnormal test results.

Purpose of the Study:

  • To adapt conditional entropy and conditional probability for feature extraction in machine learning models.
  • To predict abnormal laboratory blood test results more accurately.
  • To address limitations in previous studies on predicting test abnormalities.

Main Methods:

  • Utilized an ICU dataset from Alberta, Canada (55,689 admissions).
  • Compared two machine learning approaches: one with standard features (vitals, demographics, diagnosis) and another with added conditional entropy/probability features.
  • Evaluated performance using 4 models and 10 metrics, including AUC, sensitivity, and specificity, for 18 blood tests.

Main Results:

  • The approach incorporating conditional entropy/probability features achieved a higher average AUC (0.89) compared to standard features (0.86).
  • Significant improvements were observed in sensitivity, F1 score, and mean G across multiple tests and models with the new features.
  • Pretest probability, indicating the likelihood of normal results after consecutive normal tests, emerged as the most relevant feature.

Conclusions:

  • Conditional entropy-based features and pretest probability enhance the discrimination of normal versus abnormal lab test results.
  • These findings represent a step towards developing guidelines to reduce overtesting in the ICU.
  • The study demonstrates the potential of advanced machine learning techniques in optimizing laboratory test ordering.