Rare diseases, although individually rare, collectively affect one in ten Americans. Because of their rarity, patients with rare diseases are typically left misdiagnosed or undiagnosed, which leads to a prolonged medical journey. The diagnosis pathway of a rare disease is highly dependent on the associated clinical phenotypes, i.e., the observable characteristics, at the physical, morphologic, or biochemical level, of an individual. In our previous study, we applied a collaborative filtering model on clinical data generated at Mayo Clinic to stratify patients into subgroups of rare diseases. Information mined from clinical data, however, usually contains a certain level of noise, such as occurrences of comorbidities, which could impact the accuracy of differential diagnosis. In this study, we sought to incorporate a knowledge-driven approach into collaborative filtering to optimize results learned from clinical data. Our results demonstrated an improvement in performance over pure data-driven approaches with the potential to facilitate the differential diagnosis of rare diseases.
|Original language||English (US)|
|Number of pages||10|
|Journal||AMIA ... Annual Symposium proceedings. AMIA Symposium|
|State||Published - Jan 1 2018|
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