Discovering disease associations by integrating electronic clinical data and medical literature.

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&#...

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Autores principales:	Antony B Holmes, Alexander Hawson, Feng Liu, Carol Friedman, Hossein Khiabanian, Raul Rabadan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/42d20e901d7848fca0c51c9a26ef8a52
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spelling	oai:doaj.org-article:42d20e901d7848fca0c51c9a26ef8a522021-11-18T06:51:28ZDiscovering disease associations by integrating electronic clinical data and medical literature.1932-620310.1371/journal.pone.0021132https://doaj.org/article/42d20e901d7848fca0c51c9a26ef8a522011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21731656/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Electronic 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.Antony B HolmesAlexander HawsonFeng LiuCarol FriedmanHossein KhiabanianRaul RabadanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 6, p e21132 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Antony B Holmes Alexander Hawson Feng Liu Carol Friedman Hossein Khiabanian Raul Rabadan Discovering disease associations by integrating electronic clinical data and medical literature.
description	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.
format	article
author	Antony B Holmes Alexander Hawson Feng Liu Carol Friedman Hossein Khiabanian Raul Rabadan
author_facet	Antony B Holmes Alexander Hawson Feng Liu Carol Friedman Hossein Khiabanian Raul Rabadan
author_sort	Antony B Holmes
title	Discovering disease associations by integrating electronic clinical data and medical literature.
title_short	Discovering disease associations by integrating electronic clinical data and medical literature.
title_full	Discovering disease associations by integrating electronic clinical data and medical literature.
title_fullStr	Discovering disease associations by integrating electronic clinical data and medical literature.
title_full_unstemmed	Discovering disease associations by integrating electronic clinical data and medical literature.
title_sort	discovering disease associations by integrating electronic clinical data and medical literature.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/42d20e901d7848fca0c51c9a26ef8a52
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Discovering disease associations by integrating electronic clinical data and medical literature.

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