Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records
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Publication:3304860
DOI10.1080/01621459.2018.1549050zbMath1437.62660OpenAlexW2903626835WikidataQ100517736 ScholiaQ100517736MaRDI QIDQ3304860
Peng Wu, Yuanjia Wang, Donglin Zeng
Publication date: 3 August 2020
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539620
Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (5)
On using electronic health records to improve optimal treatment rules in randomized trials ⋮ The designed bootstrap for causal inference in big observational data ⋮ Unnamed Item ⋮ A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity ⋮ Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding
Uses Software
Cites Work
- Statistical methods for dynamic treatment regimes. Reinforcement learning, causal inference, and personalized medicine
- Performance guarantees for individualized treatment rules
- Matching methods for causal inference: a review and a look forward
- Fast rates for support vector machines using Gaussian kernels
- \({\mathcal Q}\)-learning
- Weak convergence and empirical processes. With applications to statistics
- 10.1162/15324430152733133
- Combining biomarkers to optimize patient treatment recommendations
- Support Vector Machines
- Dealing with limited overlap in estimation of average treatment effects
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning
- Full Matching in an Observational Study of Coaching for the SAT
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