ArticleNPJ digital medicine2024
Individualized decision making in on-scene resuscitation time for out-of-hospital cardiac arrest using reinforcement learning.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- [Regression analysis to calculate the time point of ROSC-A feasibility study].Die Anaesthesiologie · 2026Article
- Liver transplant donor-recipient matching with offline reinforcement learning.NPJ digital medicine · 2026Article
- Medical AI across Data Regimes to Promote Proactive Health.Health data science · 2026Review
- Optimizing Clinical Risk Management Through Motorcycle Emergency Response: A Three-Year Retrospective Study of the Khon Kaen International Marathon in Thailand.Risk management and healthcare policy · 2025Article
- Interpretable machine learning for predicting 30-day mortality following intracranial hemorrhage surgery.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
On-scene resuscitation time is associated with out-of-hospital cardiac arrest (OHCA) outcomes. We developed and validated reinforcement learning models for individualized on-scene resuscitation times, leveraging nationwide Korean data. Adult OHCA patients with a medical cause of arrest were included (N = 73,905). The optimal policy was derived from conservative Q-learning to maximize survival. The on-scene return of spontaneous circulation hazard rates estimated from the Random Survival Forest were used as intermediate rewards to handle sparse rewards, while patients' historical survival was reflected in the terminal rewards. The optimal policy increased the survival to hospital discharge rate from 9.6% to 12.5% (95% CI: 12.2-12.8) and the good neurological recovery rate from 5.4% to 7.5% (95% CI: 7.3-7.7). The recommended maximum on-scene resuscitation times for patients demonstrated a bimodal distribution, varying with patient, emergency medical services, and OHCA characteristics. Our survival analysis-based approach generates explainable rewards, reducing subjectivity in reinforcement learning.
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Registered trials
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