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

ABSTRACT:

Background

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.

Objective

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.

Materials and methods

SUBMITTER: Lambert J 

PROVIDER: S-EPMC11910865 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature