부산대학교 도서관

Problem definition : Effective hypertension management is critical to reducing the consequences of atherosclerotic cardiovascular disease, a leading cause of death in the United States. Clinical guidelines for hypertension can be enhanced using decision-analytic approaches capable of capturing complexities in treatment planning. However, model-generated recommendations may be uninterpretable/unintuitive, limiting their clinical acceptability. We address this challenge by investigating interpretable treatment plans. Methodology/results : We formulate interpretable treatment plans as Markov decision processes (MDPs) and analyze the problems of optimizing monotone policies, which prohibit decreasing treatment intensity for sicker patients, and class-ordered monotone policies , which generalize monotone policies. We establish that both policies depend on initial state distributions and that optimal monotone policies can be generated tractably for many treatment planning problems. Next, we propose exact formulations for optimizing interpretable policies broadly. Then, we analyze the price of interpretability , proving that the class-ordered monotone policy’s price of interpretability does not exceed the monotone policy’s price of interpretability. Finally, we formulate and evaluate MDPs for hypertension treatment planning using a large nationally representative data set of the U.S. population. We compare the structure and performance of optimal monotone policies and class-ordered monotone policies with optimal MDP-based policies and current clinical guidelines. At the patient level, optimal MDP-based policies may be unintuitive, recommending more aggressive treatment for healthier patients than sicker patients. Conversely, monotone policies and class-ordered monotone policies never deescalate treatment, reflecting clinical intuition. Across 66.5 million patients, optimized monotone policies and class-ordered monotone policies outperform clinical guidelines, saving over 3