Differentially Private Outcome-Weighted Learning for Optimal Dynamic Treatment Regime Estimation.

ABSTRACT: Precision medicine is a framework for developing evidence-based medical recommendations that seeks to determine the optimal sequence of treatments tailored to all of the relevant patient-level characteristics which are observable. Because precision medicine relies on highly sensitive, patient-level data, ensuring the privacy of participants is of great importance. Dynamic treatment regimes (DTRs) provide one formalization of precision medicine in a longitudinal setting. Outcome-Weighted Learning (OWL) is a family of techniques for estimating optimal DTRs based on observational data. OWL techniques leverage support vector machine (SVM) classifiers in order to perform estimation. SVMs perform classification based on a set of influential points in the data known as support vectors. The classi

SUBMITTER: Spicker D 

PROVIDER: S-EPMC11281278 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature