Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records.
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...
| Published in: | Drugs Vol. 81; no. 4; pp. 471 - 483 |
|---|---|
| Main Authors: | , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Mar2021
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149398144&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149398144 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00126667 C5A jtl: Drugs issn: 00126667 maglogo: N pubinfo: dt: Mar2021 vid: 81 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149398144 148645815 149398144 149398144 10.1007/s40265-020-01435-4 149398144 ppf: 471 ppct: 12 formats: tig: atl: Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records. aug: au: Zheng, Hua Ryzhov, Ilya O. Xie, Wei Zhong, Judy affil: Department of Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA, USA sug: subj: Electronic Health Records Individualized Medicine Comorbidity Prevention and Control Diabetes Mellitus, Type 2 Diabetic Patients Reinforcement (Psychology) Learning Methods Outcomes (Health Care) Human Artificial Intelligence Algorithms Blood Glucose Blood Pressure Cardiovascular Risk Factors Retrospective Design Prospective Studies New York Descriptive Statistics ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|