Machine learning functional impairment classification with electronic health record data

作者：Juliessa M. Pavon, Laura A. Previll, Myung Woo, Ricardo Henao, Mary Solomon, Ursula Rogers, Andrew Olson, Jonathan Fischer, Christopher Leo, Gerda G. Fillenbaum, Helen Hoenig, David Casarett · 发表于：Journal of the American Geriatrics Society · 年份：2023 · DOI：10.1111/jgs.18383 · 被引用次数：14 · 研究领域：Chronic Disease Management Strategies、Machine Learning in Healthcare、Frailty in Older Adults

BACKGROUND: Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment. METHODS: We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome. RESULTS: Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0...