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Machine learning for optimal test admission in the presence of resource constraints

Author

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  • Ramy Elitzur

    (University of Toronto)

  • Dmitry Krass

    (University of Toronto)

  • Eyal Zimlichman

    (Tel Aviv University)

Abstract

Developing rapid tools for early detection of viral infection is crucial for pandemic containment. This is particularly crucial when testing resources are constrained and/or there are significant delays until the test results are available – as was quite common in the early days of Covid-19 pandemic. We show how predictive analytics methods using machine learning algorithms can be combined with optimal pre-test screening mechanisms, greatly increasing test efficiency (i.e., rate of true positives identified per test), as well as to allow doctors to initiate treatment before the test results are available. Our optimal test admission policies account for imperfect accuracy of both the medical test and the model prediction mechanism. We derive the accuracy required for the optimized admission policies to be effective. We also show how our policies can be extended to re-testing high-risk patients, as well as combined with pool testing approaches. We illustrate our techniques by applying them to a large data reported by the Israeli Ministry of Health for RT-PCR tests from March to September 2020. Our results demonstrate that in the context of the Covid-19 pandemic a pre-test probability screening tool with conventional RT-PCR testing could have potentially increased efficiency by several times, compared to random admission control.

Suggested Citation

  • Ramy Elitzur & Dmitry Krass & Eyal Zimlichman, 2023. "Machine learning for optimal test admission in the presence of resource constraints," Health Care Management Science, Springer, vol. 26(2), pages 279-300, June.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:2:d:10.1007_s10729-022-09624-1
    DOI: 10.1007/s10729-022-09624-1
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    References listed on IDEAS

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    1. Hrayer Aprahamian & Douglas R. Bish & Ebru K. Bish, 2020. "Optimal Group Testing: Structural Properties and Robust Solutions, with Application to Public Health Screening," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 895-911, October.
    2. Douglas R Bish & Ebru K Bish & Hussein El-Hajj & Hrayer Aprahamian, 2021. "A robust pooled testing approach to expand COVID-19 screening capacity," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-15, February.
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    Cited by:

    1. Sandra Zilker & Sven Weinzierl & Mathias Kraus & Patrick Zschech & Martin Matzner, 2024. "A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis," Health Care Management Science, Springer, vol. 27(2), pages 136-167, June.

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