title: Discovering disease associations by integrating electronic clinical data and medical literature.
abstract: Electronic health record (EHR) systems offer an exceptional opportunity for studying many diseases and their associated medical conditions within a population. The increasing number of clinical record entries that have become available electronically provides access to rich, large sets of patients' longitudinal medical information. By integrating and comparing relations found in the EHRs with those already reported in the literature, we are able to verify existing and to identify rare or novel associations. Of particular interest is the identification of rare disease co-morbidities, where the small numbers of diagnosed patients make robust statistical analysis difficult. Here, we introduce ADAMS, an Application for Discovering Disease Associations using Multiple Sources, which contains various statistical and language processing operations. We apply ADAMS to the New York-Presbyterian Hospital's EHR to combine the information from the relational diagnosis tables and textual discharge summaries with those from PubMed and Wikipedia in order to investigate the co-morbidities of the rare diseases Kaposi sarcoma, toxoplasmosis, and Kawasaki disease. In addition to finding well-known characteristics of diseases, ADAMS can identify rare or previously unreported associations. In particular, we report a statistically significant association between Kawasaki disease and diagnosis of autistic disorder.
authors: Department of Biomedical Informatics, Columbia University College of Physicians and Surgeons, New York, New York, United States of America.
authors: Antony B
authors: AB
authors: Holmes
authors: Alexander
authors: A
authors: Hawson
authors: Feng
authors: F
authors: Liu
authors: Carol
authors: C
authors: Friedman
authors: Hossein
authors: H
authors: Khiabanian
authors: Raul
authors: R
authors: Rabadan
mesh_terms: D015897:Comorbidity; D019992:Databases as Topic; D004194:Disease; D057286:Electronic Health Records; D006801:Humans; D009518:New York; D039781:PubMed