An optimal policy for patient laboratory tests in intensive care units

Li Fang Cheng, Niranjani Prasad, Barbara E. Engelhardt

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

Laboratory testing is an integral tool in the management of patient care in hospitals, particularly in intensive care units (ICUs). There exists an inherent trade-off in the selection and timing of lab tests between considerations of the expected utility in clinical decision- making of a given test at a specific time, and the associated cost or risk it poses to the patient. In this work, we introduce a framework that learns policies for ordering lab tests which optimizes for this trade-off. Our approach uses batch off-policy reinforcement learning with a composite reward function based on clinical imperatives, applied to data that include examples of clinicians ordering labs for patients. To this end, we develop and extend principles of Pareto optimality to improve the selection of actions based on multiple reward function components while respecting typical procedural considerations and prioritization of clinical goals in the ICU. Our experiments show that we can estimate a policy that reduces the frequency of lab tests and optimizes timing to minimize information redundancy. We also find that the estimated policies typically suggest ordering lab tests well ahead of critical onsets- such as mechanical ventilation or dialysis|that depend on the lab results. We evaluate our approach by quantifying how these policies may initiate earlier onset of treatment.

Original languageEnglish (US)
Pages (from-to)320-331
Number of pages12
JournalPacific Symposium on Biocomputing
Volume24
Issue number2019
StatePublished - 2019
Event24th Pacific Symposium on Biocomputing, PSB 2019 - Kohala Coast, United States
Duration: Jan 3 2019Jan 7 2019

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computational Theory and Mathematics

Keywords

  • Dynamic Treatment Regimes
  • Pareto Learning
  • Reinforcement Learning

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