Encoding breast cancer patients’ medical pathways from reimbursement data using representation learning: a benchmark for clustering tasks

International audience The increasing availability of Electronic Health Records (EHRs) data presents novel opportunities to create data driven tools based on Artificial Intelligence for clinical purposes. While healthcare reimbursement data may not strictly constitute EHRs, they nonetheless offer valuable insights into patients’ medical pathways, including medical visits, procedures, and medication usage. However, the inherent complexity and dimensionality of this type of data pose significant hurdles for the direct application of Machine Learning techniques. Consequently, unsupervised methods for encoding patients’ medical data while reducing dimensionality are imperative. In this regard, representation learning emerges as a interesting approach for creating meaningful patient representations.Our contribution involves a comprehensive benchmarking assessment of three prominent representation learning paradigms. We aim to encode and cluster breast cancer patients’ medical pathways using reimbursement data extracted from the French Nationwide Healthcare Database (SNDS). Our findings underscore the limitations of solely evaluating representation learning approaches based on classical machine learning performance metrics. Thus, we advocate for evaluating the quality of the patients’ representation learning latent space through statistical analyses and developing new metrics that reflect the clinical reality of patients.