Paper Info
Title
Efficient Clinical Concept Extraction in Electronic Medical Records.
Abstract
Automatic identification of clinical concepts in electronic medical records (EMR) is useful not only in forming a complete longitudinal health record of patients, but also in recovering missing codes for billing, reducing costs, finding more accurate clinical cohorts for clinical trials, and enabling better clinical decision support. Existing systems for clinical concept extraction are mostly knowledge-driven, relying on exact match retrieval from original or lemmatized reports, and very few of them are scaled up to handle large volumes of complex, diverse data. In this demonstration we will showcase a new system for real-time detection of clinical concepts in EMR. The system features a large vocabulary of over 5.6 million concepts. It achieves high precision and recall, with good tolerance to typos through the use of a novel prefix indexing and subsequence matching algorithm, along with a recursive negation detector based on efficient, deep parsing. Our system has been tested on over 12.9 million reports of more than 200 different types, collected from 800,000+ patients. A comparison with the state of the art shows that it outperforms previous systems in addition to being the first system to scale to such large collections.
Year
Venue
Field
2017
THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Information retrieval,Computer science,Artificial intelligence,Medical record,Concept extraction,Machine learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yufan Guo116215.45
Deepika Kakrania200.68
Tyler Baldwin301.69
Tanveer Fathima Syeda-Mahmood444784.69