A data-driven architecture using natural language processing to improve phenotyping efficiency and accelerate genetic diagnoses of rare disorders

A data-driven architecture using natural language processing to improve phenotyping efficiency and accelerate genetic diagnoses of rare disorders

Summary: Effective genetic diagnosis requires the correlation of genetic variant data with detailed phenotypic information. However, manual encoding of clinical data into machine-readable forms is laborious and subject to observer bias. Natural language processing (NLP) of electronic health records...

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Bibliographic Details
Main Authors: Jignesh R. Parikh, Casie A. Genetti, Asli Aykanat, Catherine A. Brownstein, Klaus Schmitz-Abe, Morgan Danowski, Andrew Quitadomo, Jill A. Madden, Calum Yacoubian, Richard Gain, Tessa Williams, Mary Meskell, Andrew Brown, Alison Frith, Shira Rockowitz, Piotr Sliz, Pankaj B. Agrawal, Thomas Defay, Paul McDonagh, John Reynders, Sebastien Lefebvre, Alan H. Beggs
Format: Article
Language:English
Published: Elsevier 2021-07-01
Series:HGG Advances
Subjects:
natural language processing
genetics
Human Phenotype Ontology
electronic health records
Online Access:http://www.sciencedirect.com/science/article/pii/S2666247721000166

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http://www.sciencedirect.com/science/article/pii/S2666247721000166

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