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In Genetics in medicine : official journal of the American College of Medical Genetics

PURPOSE : Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments.

METHODS : We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data.

RESULTS : Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs.

CONCLUSION : Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

Chang Willie H, Mashouri Pouria, Lozano Alexander X, Johnstone Brittney, Husić Mia, Olry Annie, Maiella Sylvie, Balci Tugce B, Sawyer Sarah L, Robinson Peter N, Rath Ana, Brudno Michael

2020-May-05

crowdsourcing, machine learning, medical education, phenotype, rare diseases