Don’t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records
2016 (English)In: Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology, Association for Computational Linguistics , 2016, 95-105 p.Conference paper (Refereed)
Mental Health Records (MHRs) contain freetext documentation about patients’ suicide and suicidality. In this paper, we address the problem of determining whether grammatic variants (inflections) of the word “suicide” are af- firmed or negated. To achieve this, we populate and annotate a dataset with over 6,000 sentences originating from a large repository of MHRs. The resulting dataset has high InterAnnotator Agreement (κ 0.93). Furthermore, we develop and propose a negation detection method that leverages syntactic features of text1 . Using parse trees, we build a set of basic rules that rely on minimum domain knowledge and render the problem as binary classification (affirmed vs. negated). Since the overall goal is to identify patients who are expected 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 extraction capabilities.
Place, publisher, year, edition, pages
Association for Computational Linguistics , 2016. 95-105 p.
Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:kth:diva-204782OAI: oai:DiVA.org:kth-204782DiVA: diva2:1086114
Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology
QC 201704182017-03-312017-03-312017-04-18Bibliographically approved