King's College London

Research portal

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)
Publication statusPublished - 16 Jun 2016


King's Authors


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

Download statistics

No data available

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454