[Paper Review] Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions

Nina Deliu, Joseph Jay Williams+1 more|arXiv (Cornell University)|Mar 4, 2022

Advanced Bandit Algorithms Research132 references4 citations

TL;DR

This paper presents a unified framework for constructing optimal adaptive interventions (AIs) in healthcare using reinforcement learning (RL), bridging dynamic treatment regimes and just-in-time adaptive interventions in mobile health. It integrates RL methods—such as Q-learning, deep Q-networks, and contextual bandits—into a coherent methodology for learning optimal, data-driven treatment policies from sequential, patient-specific data, with key contributions in algorithmic design, case studies, and identifying future research directions in biostatistics and AI collaboration.

ABSTRACT

In recent years, reinforcement learning (RL) has acquired a prominent position in health-related sequential decision-making problems, gaining traction as a valuable tool for delivering adaptive interventions (AIs). However, in part due to a poor synergy between the methodological and the applied communities, its real-life application is still limited and its potential is still to be realized. To address this gap, our work provides the first unified technical survey on RL methods, complemented with case studies, for constructing various types of AIs in healthcare. In particular, using the common methodological umbrella of RL, we bridge two seemingly different AI domains, dynamic treatment regimes and just-in-time adaptive interventions in mobile health, highlighting similarities and differences between them and discussing the implications of using RL. Open problems and considerations for future research directions are outlined. Finally, we leverage our experience in designing case studies in both areas to showcase the significant collaborative opportunities between statistical, RL, and healthcare researchers in advancing AIs.

Motivation & Objective

• To address the limited real-world application of reinforcement learning (RL) in biostatistics by unifying its use across adaptive interventions (AIs).
• To bridge the methodological gap between dynamic treatment regimes (DTRs) and just-in-time adaptive interventions (JITIs) in mobile health through a common RL framework.
• To provide a comprehensive technical survey of RL methods tailored for constructing optimal, data-driven AIs in clinical and digital health settings.
• To showcase collaborative opportunities between statistical, RL, and clinical researchers in advancing personalized, adaptive care.
• To identify open problems and future research directions in optimal policy learning and RL application in biostatistics.

Proposed method

• Uses reinforcement learning (RL) as a unifying framework to model sequential decision-making in adaptive interventions, where actions (treatments) are selected based on patient history to maximize cumulative reward (e.g., health outcome).
• Applies Q-learning with function approximation to estimate optimal Q-functions and derive stage-specific treatment policies from longitudinal patient data.
• Employs deep Q-networks (DQN) with experience replay and target networks to stabilize training and enable deep RL in high-dimensional clinical data settings.
• Utilizes linear contextual bandit algorithms—LinUCB and LinTS—using feature representations of patient states to balance exploration and exploitation in real-time decision-making.
• Incorporates posterior sampling and upper confidence bounds in LinTS and Action-Centered TS to enable Bayesian exploration in sequential treatment assignment.
• Employs iterative estimation techniques such as G-estimation and regret modeling to improve stability and consistency in policy learning under potential model misspecification.

Experimental results

Research questions

• RQ1 How can reinforcement learning be systematically applied to construct optimal adaptive interventions in both traditional clinical and mobile health settings?
• RQ2 What are the key methodological similarities and differences between dynamic treatment regimes and just-in-time adaptive interventions when viewed through the RL lens?
• RQ3 How do RL algorithms such as Q-learning, DQN, and contextual bandits compare in performance and stability for learning optimal treatment policies from real-world clinical data?
• RQ4 What are the critical challenges in applying RL to healthcare, particularly regarding interpretability, stability, and clinical adoption?
• RQ5 What collaborative research pathways can emerge between biostatisticians, RL researchers, and clinicians to advance personalized, adaptive care?

Key findings

• The paper establishes that RL provides a flexible and principled framework for constructing optimal adaptive interventions across diverse clinical and digital health contexts.
• It demonstrates that Q-learning with function approximation can effectively estimate optimal treatment policies even in high-dimensional, sequential decision-making environments.
• Deep Q-networks (DQN) with experience replay and target networks significantly improve learning stability and convergence in complex, real-world clinical data settings.
• Contextual bandit methods like LinUCB and LinTS offer scalable and efficient solutions for real-time, personalized treatment decisions with theoretical guarantees on regret minimization.
• The integration of G-estimation and regret modeling enhances algorithmic stability and robustness, particularly under model misspecification or unobserved confounding.
• Case studies illustrate that RL-based AIs can outperform traditional rule-based or static treatment strategies in terms of expected clinical outcomes, especially when patient response dynamics are complex and heterogeneous.