ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2019
An Optimal Policy for Patient Laboratory Tests in Intensive Care Units.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- The effect of blood urea nitrogen (BUN)/albumin ratio in evaluating mortality in pediatric burn patients with 20% or greater total body surface area involvement.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2026Article
- Smart Imitator: Learning from Imperfect Clinical Decisions.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A proactive/reactive mass screening approach with uncertain symptomatic cases.PLoS computational biology · 2024Article
- Rise of the Machines: The Inevitable Evolution of Medicine and Medical Laboratories Intertwining with Artificial Intelligence-A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review.Journal of medical Internet research · 2020Review
- Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019.Critical care medicine · 2020Article
- Precision Medicine: Improving health through high-resolution analysis of personal data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
30864333PMC6417830What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.