Improving patient clustering by incorporating structured variable label relationships in similarity measures

Patient stratification is the cornerstone of numerous health investigations, serving to enhance the estimation of treatment efficacy and facilitating patient matching. To stratify patients, similarity measures between patients can be computed from clinical variables contained in medical health records. These variables have both values and labels structured in ontologies or other classification systems. The relevance of considering variable label relationships in the computation of patient similarity measures has been poorly studied.

We adapt and evaluate several weighted versions of the Cosine similarity in order to consider structured label relationships to compute patient similarities from a medico-administrative database.

As a use case, we clustered patients aged 60 years from their annual medicine reimbursements contained in the Échantillon Généraliste des Bénéficiaires, a random sample of a French medico-administrative database. We used four patient similarity measures: the standard Cosine similarity, a weighted Cosine similarity measure that includes variable frequencies and two weighted Cosine similarity measures that consider variable label relationships. We construct patient networks from each similarity measure and identify clusters of patients using the Markov Cluster algorithm. We evaluate the performance of the different similarity measures with enrichment tests based on patient diagnoses.

The weighted similarity measures that include structured variable label relationships perform better to identify similar patients. Indeed, using these weighted measures, we identify more clusters associated with different diagnose enrichment. Importantly, the enrichment tests provide clinically interpretable insights into these patient clusters.

Considering label relationships when computing patient similarities improves stratification of patients regarding their health status.