ArticleHealth care management science2015
Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity.
Article in Health care management science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Review
- Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.Health care management science · 2025Review
- In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Application of Artificial Intelligence in Diagnosis of Craniopharyngioma.Frontiers in neurology · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigate the problem faced by a healthcare system wishing to allocate its constrained screening resources across a population at risk for developing a disease. A patient's risk of developing the disease depends on his/her biomedical dynamics. However, knowledge of these dynamics must be learned by the system over time. Three classes of reinforcement learning policies are designed to address this problem of simultaneously gathering and utilizing information across multiple patients. We investigate a case study based upon the screening for Hepatocellular Carcinoma (HCC), and optimize each of the three classes of policies using the indifference zone method. A simulation is built to gauge the performance of these policies, and their performance is compared to current practice. We then demonstrate how the benefits of learning-based screening policies differ across various levels of resource scarcity and provide metrics of policy performance.
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25308168What 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.