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Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study.
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Ministry of Health Clinical Practice Guidelines: Diabetes Mellitus
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Ministry of Health Clinical Practice Guidelines: Hypertension
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Establishment of the SingHealth Diabetes Registry
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Neural Information Processing Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Reinforcement Learning in Healthcare: From Treatment Optimization to the Challenge of Explainability with Large Language Models
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Reinforcement learning-based expanded personalized diabetes treatment recommendation using South Korean electronic health records
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Effective data-driven precision medicine by cluster-applied deep reinforcement learning
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Hybrid Stacked Autoencoder-Graph Attention Network with Chebyshev Cloud Drift Optimization for Treatment Recommendation
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Hypertension in Adults With Diabetes in Southeast Asia: A Systematic Review
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Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
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Addressing Unmet Needs in the Management of Diabetes in Asia and Enhancing Diabetes Care Through the Use of Digital Technology:
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Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study
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Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence–Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study
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Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study
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Applications of AI in Predicting Drug Responses for Type 2 Diabetes
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Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework
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Applications of Artificial Intelligence in predicting drug responses for Type 2 Diabetes (Preprint)
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Assessment of Large Language Models for Enhancing Diabetologist-Developed Personalized Treatment Plans in Complex Type 2 Diabetes
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Federated multimodal AI for precision-equitable diabetes care
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Treatment of type 2 diabetes mellitus - a current view of the different drug classes and strategies for their use
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