DEVELOPING MACHINE LEARNING METHODOLOGY FOR PRECISION HEALTH

Abstract

Precision health has been an increasingly popular solution to improve health care quality and guide the decision making process. This includes precision medicine (at the individual level) and precision public health (at the population level such as communities and institutions). By learning from the available medical data with advanced analytical tools, precision health recommends the treatments that are individualized to each patient or entity to maximize clinical outcomes for each individual. We extend and develop three machine learning methods to improve the estimation of optimal individualized treatment regimes in precision health: the jackknife estimator of value function of precision medicine models compared with zero-order models, doubly robust outcome-weighted estimators with deep neural network structures for complex and large data, and risk-adjusted adverse event monitoring for survival data. First, motivated by a knee osteoarthristis trial, we estimate value functions and select the optimal treatment with the jackknife method whose consistency is established under weak assumptions. Next, we implement deep learning architecture in augmented outcome-weighted learning to increase model flexibility and computation efficiency, especially for high-dimensional data such as medical imaging. Lastly, we develop a risk-adjusted survival model to monitor adverse events and estimate its variance for hierarchical, right-censored data with recurrent events. All three methodologies aim to solve practical, health-related challenges and provide data-driven decision support and operations.Doctor of Philosoph