Deep Learning for Large-Scale and Complex-Structured Biomedical Data

Abstract

In this dissertation, we propose novel Deep Neural Network (DNN) based statistical learning models that can provide accurate predictions and clear interpretations simultaneously. Chapter 1 presents an introduction to the DNN as a nonparametric approximator to complex, non-linear functions.

Chapter 2 introduces the Interpretable Neural Network Regression (INNER), a logistic regression model with nonparametric covariate-dependent coefficients constructed by DNNs. Applied to the individualized risk assessment of preoperative opioid use, the proposed INNER model can predict preoperative opioid use based on the preoperative characteristics and estimate the individual-level odds of opioid use induced by overall body pain, leading to straightforward interpretations of the tendency to use opioids. Applying INNER to Analgesic Outcomes Study (AOS), we identify patient characteristics strongly associated with opioid use.

Chapter 3 develops the Penalized Deep Partially Linear Cox Model (Penalized DPLC) that incorporates the SCAD penalty to select significant features and employs the DNN to estimate the nonparametric component of the partially linear Cox model. An efficient alternating optimization algorithm is used for model estimation. We also prove the convergence and asymptotic properties of the estimator. The merits of this method are shown through intensive simulations. Finally, the Penalized DPLC is applied to the National Lung Screening Trial (NLST) to uncover the effects of critical clinical and imaging risk factors on patients' survival.

Chapter 4 presents the Deep Survival Learner (DSL) for estimating the Conditional Average Treatment Effects (CATEs) in survival settings. DSL adapts the Doubly-Robust Learner to right-censored data by Inverse Probability of Censoring Weights (IPCW). DNNs are used as base learners to account for the complex relationships between baseline characteristics and survival outcomes. Large-scale simulation experiments are conducted to assess the performance of the proposed model under various scenarios. We then use DSL to study the treatment heterogeneity of perioperative chemotherapy for patients from the Boston Lung Cancer Study (BLCS).