Quantitative Issues in Cancer Research Working Group Seminar
Kimberly Greco, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health
Graph Attention Framework to Enhance Rare Disease Sub-Phenotyping from EHR
Abstract: Accurately sub-phenotyping patients according to their risk for an adverse clinical outcome can significantly enhance clinical decision-making. Recent advances in patient representation learning have enabled the development of sophisticated clustering algorithms designed to accurately sub-phenotype patients in ways that are predictive of these outcomes. To optimize data for clustering, we introduce a methodology utilizing a Graph Attention Network (GAT) to enhance Electronic Health Record (EHR) code-level embeddings. This approach facilitates the generation of rich patient-level embeddings, which are then leveraged in downstream clustering tasks aimed at sub-phenotyping patients based on their risk of experiencing a particular outcome. Building on this foundation, we explore ongoing work focused on advancing personalized medicine for patients with rare diseases.
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