Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

ABSTRACT:

Background

Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients.

Objective

To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients.

Design

We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization

SUBMITTER: Yan J 

PROVIDER: S-EPMC6374273 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature