Campanella Lab

Research focus

NIH R01 · 2025

Multi-modal unsupervised embeddings to advance machine learning in healthcare

PROJECT SUMMARY Integrating high-dimensional and heterogenous biomedical data, such as electronic health records (EHRs), molecular data, imaging, and free text, is a key challenge for making robust discoveries that transform healthcare. Current work in the literature commonly analyze biomedical data types separately, focus on small disease-related cohorts of patients, and rely on domain experts and manual clinical feature selection in an ad hoc manner. Although appropriate in some situations, supervised definitions of the feature space scale poorly, do not generalize well, include inherent biases, and miss opportunities to discover novel patterns and features. To address these issues, we…

From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.