Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"Dynamic treatment regimes"”.
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Classification-based method for estimating dynamic treatment regimes
Dynamic treatment regimes are sequential decision rules dictating how to individualize treatments to patients based on evolving treatments and covariate history. In this thesis, we investigate two methods of estimating dynamic treatment regimes. The first method extends outcome weighted learning …
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Modifications of Q-learning to Optimize Dynamic Treatment Regimes
… it facilitates the construction and analysis of dynamic treatment regimes (DTRs). There exists a comprehensive body of literature on various statistical methods to analyze data collected from such trials and estimate the optimal DTR for an individual subject, among which Q-learning with linear …
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On Dynamic Treatment Regimes: Collaborative Search and LLM-Driven Decision Trees
This thesis evaluates the validity of current dynamic treatment regime algorithms and presents a novel data structure for extracting treatment decisions from unstructured clinical notes. The main contribution is the Clinical Decision Tree (CDT) which uses large language models (LLMs) to extract key …
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A Recurrent Network Approach to G-Computation for Sepsis Outcome Prediction Under Dynamic Treatment Regimes
… to understand the relationship between sepsis treatment, fluid overload, mortality, and other outcomes, most are correlation-based and cannot actually estimate the causal effects of intervention. Prospective randomized clinical trials allow researchers to test the effects of alternative …
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Uncertainty Quantification in Deep Learning Models of G-Computation for Outcome Prediction under Dynamic Treatment Regimes
… making counterfactual predictions and estimating treatment effects under dynamic and time-varying treatment regimes. Two G-Net models have been successfully implemented: one that uses recurrent neural networks (RNNs) as its predictors, and one that uses transformer encoders (G-Transformer). …
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Benchmarking Reinforcement Learning and Off Policy Evaluation for Medical Decision Making
… risks, low data availability, short treatment episodes, sparse re[1]wards, partial observations, and heterogeneous treatment effects (HTE). Despite significant interest in developing Dynamic Treatment Regimes (DTRs) for longitudinal patient care scenarios, no standardized benchmark …
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Bayesian Pharmacokinetic Models for Inference and Optimal Sequential Decision Making with Applications in Personalized Medicine
… in drug response is an obstacle to optimal treatment. Pharmacokinetic modelling offers one approach to studying drug response, often with covariate focused dose adjustment criteria being reported along side pharmacokinetic modelling. However, excess variation in concentrations continues to …
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Enhancing the Reliability of Real-World Evidence and Clinical Decision-Making: Robust, Calibrated, and Uncertainty-Aware Methods for Observational Healthcare Research
… research settings. ALPHA efficiently estimates treatment effects without stringent modeling assumptions and accommodates site-specific heterogeneity across decentralized data networks without sharing individual-level information. Applying ALPHA to electronic health record data from eight …
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Statistical reinforcement learning for individualized decision making
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Statistical Methods for Off-Policy Learning
… mainly been studied in two settings: dynamic treatment regimes (DTRs), which focus on controlling for confounding in medical problems with short decision horizons, and Markov decision processes (MDPs) in reinforcement learning (RL), which focus on dimension reduction in closed systems …