Communication-Efficient Topic Modeling for Identifying Ischemic Stroke Subtypes from Multi-Institutional Unstructured Clinical Notes

Thomas M. Menino Convention & Exhibition Center 

Unstructured clinical notes contain comprehensive and highly relevant clinical information regarding the conditions and treatment of patients with acute ischemic stroke. Such information is critical for identifying nuanced acute stroke patient subtypes that can inform appropriate therapeutic strategies. Although topic modeling is a powerful tool for identifying patient subtypes from clinical notes, applying it at scale across healthcare institutions remains challenging because of privacy constraints and data heterogeneity. We introduce Federated Topic-SCORE, a communication-efficient algorithm that extends a spectral topic modeling framework to multi-institutional settings without sharing patient-level data. By transmitting only summary statistics that locally estimate low-rank singular subspace of topic loadings in a single round of communication, the method accurately recovers global singular subspace and topic loadings shared across sites. In simulations, Federated Topic-SCORE closely approximates the performance of pooled analyses and substantially outperforms models trained solely on individual institutions, particularly when topic weight distributions differ across sites. When implemented on multi-institutional clinical notes to identify etiological subtype topics for ischemic stroke hospitalizations, the federated model successfully recovers stroke subtypes aligned with known categories such as cardioembolic, large-artery atherosclerosis, and small vessel disease, while also revealing clinically meaningful variations within embolic presentations. These findings highlight the utility of one-shot federated topic modeling for scalable, privacy-preserving analysis of multi-institutional unstructured clinical notes.

Data Integration

Federated Learning

Representation Learning

Clinical Notes

Electronic Health Records

Cerebrovascular Disorders 

Section on Statistics in Genomics and Genetics