Open AccessDevelopment and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies
Author(s) -
Majid Afshar,
Dmitriy Dligach,
Brihat Sharma,
Xiaoyuan Cai,
Jason Boyda,
Steven Birch,
Daniel Valdez,
Suzan Zelisko,
Cara Joyce,
François Modave,
Ron Price
Publication year - 2019
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocz068
Subject(s) - computer science , data warehouse , natural language processing , architecture , information extraction , benchmark (surveying) , artificial intelligence , information retrieval , byte , identifier , server , scale (ratio) , database , world wide web , art , physics , geodesy , quantum mechanics , visual arts , programming language , geography , operating system
Natural language processing (NLP) engines such as the clinical Text Analysis and Knowledge Extraction System are a solution for processing notes for research, but optimizing their performance for a clinical data warehouse remains a challenge. We aim to develop a high throughput NLP architecture using the clinical Text Analysis and Knowledge Extraction System and present a predictive model use case.
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