A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data.

Journal: Journal of endocrinological investigation
Published Date: Dec 31, 2023

Abstract

OBJECTIVE: To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH).

Authors

  • C C McDaniel
    Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, 4306 Walker Building, Auburn, AL, 36849, USA. cnc0027@auburn.edu.
  • W-H Lo-Ciganic
    Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.
  • J Huang
    Guangxi Key Laboratory of AIDS Prevention and Treatment & Guangxi Universities Key Laboratory of Prevention and Control of Highly Prevalent Disease, School of Public Health, Guangxi Medical University,Nanning 530021, Guangxi,China.
  • C Chou
    Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, 4306 Walker Building, Auburn, AL, 36849, USA.

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