Deep learning for diagnosing patients with rare genetic diseases

Abstract

There are more than 7,000 rare diseases, some of which affect 3,500 or fewer patients in the US. Due to clinicians’ limited experience with such diseases and the considerable heterogeneity of their clinical presentations, many patients with rare genetic diseases remain undiagnosed. While artificial intelligence has demonstrated success in assisting diagnosis, its success is usually contingent on the availability of large labeled datasets. Here, we presentshepherd, a deep learning approach for multi-faceted rare disease diagnosis.shepherdis guided by existing knowledge of diseases, phenotypes, and genes to learn novel connections between a patient’s clinico-genetic information and phenotype and gene relationships. We trainshepherdexclusively on simulated patients and evaluate on a cohort of 465 patients representing 299 diseases (79% of genes and 83% of diseases are represented in only a single patient) in the Undiagnosed Diseases Network.shepherdexcels at several diagnostic facets: performing causal gene discovery (causal genes are predicted at rank = 3.52 on average), retrieving “patients-like-me” with the same gene or disease, and providing interpretable characterizations of novel disease presentations.shepherddemonstrates the potential of artificial intelligence to accelerate the diagnosis of rare disease patients and has implications for the use of deep learning on medical datasets with very few labels.