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- Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications42
This work introduces a practical and theoretically grounded transition sampling approach to address action imbalance during offline RL training and shows that the proposed approach enables substantial improvements in expected health outcomes and in consistency with relevant practice and safety guidelines.
- High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study26
High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.
- A drug mix and dose decision algorithm for individualized type 2 diabetes management6
An AI Drug mix and dose Advisor for glycemic management that provides drug-dose recommendations to improve outcomes for individual T2D patients, it could be used for clinical decision support at point-of-care, especially in resource-limited settings.
- Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption1
The proposed solution achieves over 99.9% accuracy relative to plaintext analyses, while maintaining scalable runtime performance with increasing data size and number of participating sites, demonstrate the feasibility of secure federated analytics for practical bioinformatics applications involving sensitive data.
- Graph-Based Modeling of Behavioral Transitions for Digital Health Applications: Trajectory Analysis Framework–
A graph-based behavioral trajectory model that models behavioral transitions as shortest paths through user similarity graphs provides an interpretable framework for modeling behavioral transitions in digital health applications and captures key dynamics that support personalized intervention design.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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