title: DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx.
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.
authors: School of Informatics and Computing, Indiana University, Indianapolis, IN, USA; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors: Saeed
authors: S
authors: Mehrabi
authors: School of Informatics and Computing, Indiana University, Indianapolis, IN, USA.
authors: Anand
authors: A
authors: Krishnan
authors: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors: Sunghwan
authors: S
authors: Sohn
authors: Department of Surgery, Indiana University, Indianapolis, IN, USA.
authors: Alexandra M
authors: AM
authors: Roch
authors: Department of Surgery, Indiana University, Indianapolis, IN, USA.
authors: Heidi
authors: H
authors: Schmidt
authors: Regenstrief Institute, Indianapolis, IN, USA.
authors: Joe
authors: J
authors: Kesterson
authors: Regenstrief Institute, Indianapolis, IN, USA.
authors: Chris
authors: C
authors: Beesley
authors: Regenstrief Institute, Indianapolis, IN, USA.
authors: Paul
authors: P
authors: Dexter
authors: Department of Surgery, Indiana University, Indianapolis, IN, USA.
authors: C
authors: C
authors: Max Schmidt
authors: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. Electronic address: liu.hongfang@mayo.edu.
authors: Hongfang
authors: H
authors: Liu
authors: School of Informatics and Computing, Indiana University, Indianapolis, IN, USA. Electronic address: mpalakal@iupui.edu.
authors: Mathew
authors: M
authors: Palakal
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing
keywords: Dependency parser; Natural language processing; Negation