论文标题
使用电子健康记录的增强学习，针对2型糖尿病患者的个性化多种疾病管理
Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records
论文作者
论文摘要
在2型糖尿病患者中，合并症慢性病很常见。我们开发了一种基于增强学习（RL）的人工智能算法，用于个性化的糖尿病和多种多人管理，具有相对于当前临床实践的强大潜力，可以改善健康结果。在本文中，我们使用回顾性糖果糖糖，血压和心血管疾病（CVD）风险，因为健康结果是使用回顾性的同伴组成的16,665例纽约大学Langone Health Health Care Electronic Electronic Electronic Electronic Health在2009年至2017年的2型糖尿病患者，我们在2009年至2017年进行了培训。每次相遇的历史。对RL建议对患者的独立子集进行了评估。结果表明，针对2型糖尿病的拟议个性化增强学习规定性框架与临床医生的处方相一致，以及血压，血压，心血管疾病风险的结果的实质性改善。
Comorbid chronic conditions are common among people with type 2 diabetes. We developed an Artificial Intelligence algorithm, based on Reinforcement Learning (RL), for personalized diabetes and multi-morbidity management with strong potential to improve health outcomes relative to current clinical practice. In this paper, we modeled glycemia, blood pressure and cardiovascular disease (CVD) risk as health outcomes using a retrospective cohort of 16,665 patients with type 2 diabetes from New York University Langone Health ambulatory care electronic health records in 2009 to 2017. We trained a RL prescription algorithm that recommends a treatment regimen optimizing patients' cumulative health outcomes using their individual characteristics and medical history at each encounter. The RL recommendations were evaluated on an independent subset of patients. The results demonstrate that the proposed personalized reinforcement learning prescriptive framework for type 2 diabetes yielded high concordance with clinicians' prescriptions and substantial improvements in glycemia, blood pressure, cardiovascular disease risk outcomes.
