Using Unsupervised Machine Learning to Identify Subgroups Among Home Health Patients With Heart Failure Using Telehealth

Abstract

This study explored the use of unsupervised machine learning to identify subgroups of patients with heart failure who used telehealth services in the home health setting, and examined intercluster differences for patient characteristics related to medical history, symptoms, medications, psychosocial assessments, and healthcare utilization. Using a feature selection algorithm, we selected seven variables from 557 patients for clustering. We tested three clustering techniques: hierarchical, k-means, and partitioning around medoids. Hierarchical clustering was identified as the best technique using internal validation methods. Intercluster differences among patient characteristics and outcomes were assessed with either χ test or one-way analysis of variance. Ranging in size from 153 to 233 patients, three clusters displayed patterns that differed significantly (P < .05) in patient characteristics of age, sex, medical history of comorbid conditions, use of beta blockers, and quality of life assessment. Significant (P < .001) intercluster differences in number of medications, comorbidities, and healthcare utilization were also revealed. The study identified patterns of association between (1) mental health status, pulmonary disorders, and obesity, and (2) healthcare utilization for patients with heart failure who used telehealth in the home health setting. Study results also revealed a lack of prescription guideline-recommended heart failure medications for the subgroup with the highest proportion of older female adults.