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 dependency parsing into NegEx, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs.
journal_name
J Biomed Informjournal_title
Journal of biomedical informaticsauthors
Mehrabi S,Krishnan A,Sohn S,Roch AM,Schmidt H,Kesterson J,Beesley C,Dexter P,Max Schmidt C,Liu H,Palakal Mdoi
10.1016/j.jbi.2015.02.010subject
Has Abstractpub_date
2015-04-01 00:00:00pages
213-9eissn
1532-0464issn
1532-0480pii
S1532-0464(15)00043-Xjournal_volume
54pub_type
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