Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Nov 28, 2021
Date Accepted: Apr 21, 2022
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Predicting abnormal laboratory blood test results in the intensive care unit using novel features based on information theory and historical conditional probability
ABSTRACT
Background:
Redundancy in laboratory blood tests is common in intensive care units (ICU), affecting patients' health and increasing healthcare expenses. Medical communities have made recommendations to order laboratory tests more judiciously. Wise selection can rely on modern data-driven approaches that have been shown to help identify redundant laboratory blood tests in ICUs. However, most of these works have been developed for highly selected clinical conditions such as gastrointestinal bleeding. Moreover, features based on conditional entropy and conditional probability distribution have not been used to inform the need for performing a new test.
Objective:
We aimed to address the limitations of previous works by adapting conditional entropy and conditional probability to extract features to predict abnormal laboratory blood test results.
Methods:
We used an ICU dataset collected across Alberta, Canada which included 55,689 ICU admissions from 48,672 patients with different diagnoses. We investigated conditional entropy and conditional probability-based features by comparing the performances of two machine learning approaches to predict normal and abnormal results for 18 blood laboratory tests. Approach 1 used patients' vitals, age, sex, admission diagnosis, and other laboratory blood test results as features. Approach 2 used the same features plus the new conditional entropy and conditional probability-based features.
Results:
Across the 18 blood laboratory tests, both Approach 1 and Approach 2 achieved a median F1-score, AUC, precision-recall AUC, and Gmean above 80%. We found that the inclusion of the new features statistically significantly improved the capacity to predict abnormal laboratory blood test results in between ten and fifteen laboratory blood tests depending on the machine learning model.
Conclusions:
Our novel approach with promising prediction results can help reduce over-testing in ICUs, as well as risks for patients and healthcare systems. Clinical Trial: N/A
Citation
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