Patient Clustering via Integrated Profiling of Clinical and Digital Data

TL;DR

This paper presents a new patient clustering model that combines clinical and digital interaction data using constrained low-rank approximation, resulting in improved clustering and recommendation performance in healthcare settings.

Contribution

The paper introduces a novel profile-based clustering method that integrates clinical and digital data with a constrained low-rank approximation approach for healthcare applications.

Findings

Superior clustering coherence compared to baselines

Enhanced recommendation accuracy

Effective low-dimensional patient representations

Abstract

We introduce a novel profile-based patient clustering model designed for clinical data in healthcare. By utilizing a method grounded on constrained low-rank approximation, our model takes advantage of patients' clinical data and digital interaction data, including browsing and search, to construct patient profiles. As a result of the method, nonnegative embedding vectors are generated, serving as a low-dimensional representation of the patients. Our model was assessed using real-world patient data from a healthcare web portal, with a comprehensive evaluation approach which considered clustering and recommendation capabilities. In comparison to other baselines, our approach demonstrated superior performance in terms of clustering coherence and recommendation accuracy.