Finding Rare Disease Patients in EHR Databases via Lightly-Supervised Learning
Colbaugh, R.; Glass, K.
Show abstract
There is considerable interest in developing computational models capable of detecting rare disease patients in population-scale databases such as electronic health records (EHRs). Deriving these models is challenging for several reasons, perhaps the most daunting being the limited number of already-diagnosed, labeled patients from which to learn. We overcome this obstacle with a novel lightly-supervised algorithm that leverages unlabeled and/or unreliably-labeled patient data - which is typically plentiful - to facilitate model induction. Importantly, we prove the algorithm is safe: adding unlabeled/unreliably-labeled data to the learning procedure produces models which are usually more accurate, and guaranteed never to be less accurate, than models learned from reliably-labeled data alone. The proposed method is shown to substantially outperform state-of-the-art models in patient-finding experiments involving two different rare diseases and a country-scale EHR database. Additionally, we demonstrate feasibility of transforming high-performance models generated through light supervision into simpler models which, while still accurate, are readily-interpretable by non-experts.
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