ArticleScientific reports2021
Optimal treatment recommendations for diabetes patients using the Markov decision process along with the South Korean electronic health records.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Identifying Patients at Risk for Alcohol-Exposed Pregnancies: The Importance of Addressing Multiple Risk Factors.Substance use & addiction journal · 2025Article
- Simulation Optimization of Spatiotemporal Dynamics in 3D Geometries.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- A drug mix and dose decision algorithm for individualized type 2 diabetes management.NPJ digital medicine · 2024Article
- Data-driven meal events detection using blood glucose response patterns.BMC medical informatics and decision making · 2023Article
- Diabetes medication recommendation system using patient similarity analytics.Scientific reports · 2022Article
- Wearable chemical sensors for biomarker discovery in the omics era.Nature reviews. Chemistry · 2022Review
- An interpretable RL framework for pre-deployment modeling in ICU hypotension management.NPJ digital medicine · 2022Article
- A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process.PharmacoEconomics · 2022Review
- Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The extensive utilization of electronic health records (EHRs) and the growth of enormous open biomedical datasets has readied the area for applications of computational and machine learning techniques to reveal fundamental patterns. This study's goal is to develop a medical treatment recommendation system using Korean EHRs along with the Markov decision process (MDP). The sharing of EHRs by the National Health Insurance Sharing Service (NHISS) of Korea has made it possible to analyze Koreans' medical data which include treatments, prescriptions, and medical check-up. After considering the merits and effectiveness of such data, we analyzed patients' medical information and recommended optimal pharmaceutical prescriptions for diabetes, which is known to be the most burdensome disease for Koreans. We also proposed an MDP-based treatment recommendation system for diabetic patients to help doctors when prescribing diabetes medications. To build the model, we used the 11-year Korean NHISS database. To overcome the challenge of designing an MDP model, we carefully designed the states, actions, reward functions, and transition probability matrices, which were chosen to balance the tradeoffs between reality and the curse of dimensionality issues.
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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 Socratic graph.