Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records

Abstract

Background: 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 multimorbidity management, with strong potential to improve health outcomes relative to current clinical practice. Methods: 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 2009–2017. We trained an 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. Results: The single-outcome optimization RL algorithms, RL–glycemia, RL–blood pressure, and RL–CVD, recommended consistent prescriptions as that observed by clinicians in 86.1%, 82.9%, and 98.4% of the encounters, respectively. For patient encounters in which the RL recommendations differed from the clinician prescriptions, significantly fewer encounters showed uncontrolled glycemia (A1c > 8% in 35% of encounters), uncontrolled hypertension (blood pressure > 140 mmHg in 16% of encounters), and high CVD risk (risk > 20% in 25% of encounters) under RL algorithms compared with those observed under clinicians (43%, 27%, and 31% of encounters, respectively; all p < 0.001). Conclusions: A personalized RL prescriptive framework for type 2 diabetes yielded high concordance with clinicians’ prescriptions, and substantial improvements in glycemia, blood pressure, and CVD risk outcomes.