DeepAI AI Chat
Log In Sign Up

Evaluating Reinforcement Learning Algorithms in Observational Health Settings

05/31/2018
by   Omer Gottesman, et al.
4

Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL) is a sub-field within machine learning that is concerned with learning how to make sequences of decisions so as to optimize long-term effects. Already, RL algorithms have been proposed to identify decision-making strategies for mechanical ventilation, sepsis management and treatment of schizophrenia. However, before implementing treatment policies learned by black-box algorithms in high-stakes clinical decision problems, special care must be taken in the evaluation of these policies. In this document, our goal is to expose some of the subtleties associated with evaluating RL algorithms in healthcare. We aim to provide a conceptual starting point for clinical and computational researchers to ask the right questions when designing and evaluating algorithms for new ways of treating patients. In the following, we describe how choices about how to summarize a history, variance of statistical estimators, and confounders in more ad-hoc measures can result in unreliable, even misleading estimates of the quality of a treatment policy. We also provide suggestions for mitigating these effects---for while there is much promise for mining observational health data to uncover better treatment policies, evaluation must be performed thoughtfully.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/09/2021

Challenges for Reinforcement Learning in Healthcare

Many healthcare decisions involve navigating through a multitude of trea...
06/03/2020

Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains

Reinforcement learning algorithms have had tremendous successes in onlin...
10/14/2022

A Reinforcement Learning Approach to Estimating Long-term Treatment Effects

Randomized experiments (a.k.a. A/B tests) are a powerful tool for estima...
10/08/2020

Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies

Treatment policies learned via reinforcement learning (RL) from observat...
07/31/2020

IntelligentPooling: Practical Thompson Sampling for mHealth

In mobile health (mHealth) smart devices deliver behavioral treatments r...