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Don’t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records

Research output: Chapter in Book/Report/Conference proceedingConference paper

George Gkotsis, Sumithra Velupillai, Anika Oellrich, Harry Dean, Maria Liakata, Rina Dutta

Original languageEnglish
Title of host publicationThe Third Computational Linguistics and Clinical Psychology Workshop (CLPsych)
Pages95-105
Publication statusPublished - 16 Jun 2016

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Abstract

Mental Health Records (MHRs) contain free- text documentation about patients’ suicide and suicidality. In this paper, we address the prob- lem of determining whether grammatic vari- ants (inflections) of the word “suicide” are af- firmed or negated. To achieve this, we pop- ulate and annotate a dataset with over 6,000 sentences originating from a large repository of MHRs. The resulting dataset has high Inter- Annotator Agreement ( κ 0.93). Furthermore, we develop and propose a negation detection method that leverages syntactic features of text 1 . Using parse trees, we build a set of ba- sic rules that rely on minimum domain knowl- edge and render the problem as binary clas- sification (affirmed vs. negated). Since the overall goal is to identify patients who are ex- pected to be at high risk of suicide, we focus on the evaluation of positive (affirmed) cases as determined by our classifier. Our negation detection approach yields a recall (sensitivity) value of 94.6% for the positive cases and an overall accuracy value of 91.9%. We believe that our approach can be integrated with other clinical Natural Language Processing tools in order to further advance information extrac- tion capabilities

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