DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx

Journal of Biomedical Informatics
Saeed MehrabiMathew Palakal

Abstract

In Electronic Health Records (EHRs), much of valuable information regarding patients' conditions is embedded in free text format. Natural language processing (NLP) techniques have been developed to extract clinical information from free text. One challenge faced in clinical NLP is that the meaning of clinical entities is heavily affected by modifiers such as negation. A negation detection algorithm, NegEx, applies a simplistic approach that has been shown to be powerful in clinical NLP. However, due to the failure to consider the contextual relationship between words within a sentence, NegEx fails to correctly capture the negation status of concepts in complex sentences. Incorrect negation assignment could cause inaccurate diagnosis of patients' condition or contaminated study cohorts. We developed a negation algorithm called DEEPEN to decrease NegEx's false positives by taking into account the dependency relationship between negation words and concepts within a sentence using Stanford dependency parser. The system was developed and tested using EHR data from Indiana University (IU) and it was further evaluated on Mayo Clinic dataset to assess its generalizability. The evaluation results demonstrate DEEPEN, which incorporates d...Continue Reading

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Citations

Nov 1, 2017·Journal of Digital Imaging·Daniel J Goff, Thomas W Loehfelm
Jul 14, 2019·Journal of the American Medical Informatics Association : JAMIA·Long ChenYang Huang
Feb 12, 2020·Journal of the American Medical Informatics Association : JAMIA·Chen LinTimothy A Miller
Sep 13, 2019·Journal of Healthcare Engineering·Zhengru ShenMarco Spruit
May 7, 2019·Frontiers in Medicine·Michela AssaleFederico Cabitza
Jun 6, 2020·NAR Genomics and Bioinformatics·Mengge ZhaoKai Wang
Jan 24, 2021·Computers in Biology and Medicine·Luke T SlaterGeorgios V Gkoutos
Apr 9, 2021·BMC Medical Informatics and Decision Making·Eva S KlappeRonald Cornet
Aug 30, 2021·Journal of Biomedical Informatics·Sankaran NarayananSreeranga P Rajan

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