CS Seminar
| Title: Few Shot Learning for Rare Disease Diagnosis |
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| Seminar: Computer Science |
| Speaker: Emily Alsentzer, MIT & Harvard |
| Contact: Vaidy Sunderam, VSS@emory.edu |
| Date: 2022-12-09 at 1:00PM |
| Venue: https://emory.zoom.us/j/95719302738 |
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| Abstract: Rare diseases affect 300-400 million people worldwide, yet each disease affects no more than 50 per 100,000 individuals. Many patients with rare genetic conditions remain undiagnosed due to clinicians' lack of experience with the individual diseases and the considerable heterogeneity of clinical presentations. Machine-assisted diagnosis offers the opportunity to shorten the diagnostic delays for rare disease patients. Recent advances in deep learning have considerably improved the accuracy of medical diagnosis. However, much of the success thus far is contingent on the availability of large, annotated datasets. Machine-assisted rare disease diagnosis necessitates that approaches learn from limited data and extrapolate beyond training distribution to novel genetic conditions. In this talk, I will present our work towards developing few-shot methods that can overcome the data limitations of deep learning to diagnose patients with rare genetic conditions. To infuse external knowledge into models, we first develop novel graph neural network methods for subgraph representation learning that encode how subgraphs (e.g., a set of patient phenotypes) relate to a larger knowledge graph. We leverage these advances to develop SHEPHERD, a geometric deep learning method that reasons over biomedical knowledge to diagnose patients with rare–even novel–genetic conditions. SHEPHERD operates at multiple facets throughout the rare disease diagnosis process: performing causal gene discovery, retrieving "patients-like-me", and providing interpretable characterizations of novel disease presentations. Our work demonstrates the potential for deep learning methods to accelerate the diagnosis of rare disease patients and has implications for the use of deep learning on medical datasets with very few labels. |
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