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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

Standard

Don’t Let Notes Be Misunderstood : A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records. / Gkotsis, George; Velupillai, Sumithra; Oellrich, Anika; Dean, Harry; Liakata, Maria; Dutta, Rina.

The Third Computational Linguistics and Clinical Psychology Workshop (CLPsych). 2016. p. 95-105.

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

Harvard

Gkotsis, G, Velupillai, S, Oellrich, A, Dean, H, Liakata, M & Dutta, R 2016, Don’t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records. in The Third Computational Linguistics and Clinical Psychology Workshop (CLPsych). pp. 95-105. <http://hollingk.github.io/CLPsych/pdf/CLPsych10.pdf>

APA

Gkotsis, G., Velupillai, S., Oellrich, A., Dean, H., Liakata, M., & Dutta, R. (2016). Don’t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records. In The Third Computational Linguistics and Clinical Psychology Workshop (CLPsych) (pp. 95-105) http://hollingk.github.io/CLPsych/pdf/CLPsych10.pdf

Vancouver

Gkotsis G, Velupillai S, Oellrich A, Dean H, Liakata M, Dutta R. Don’t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records. In The Third Computational Linguistics and Clinical Psychology Workshop (CLPsych). 2016. p. 95-105

Author

Gkotsis, George ; Velupillai, Sumithra ; Oellrich, Anika ; Dean, Harry ; Liakata, Maria ; Dutta, Rina. / Don’t Let Notes Be Misunderstood : A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records. The Third Computational Linguistics and Clinical Psychology Workshop (CLPsych). 2016. pp. 95-105

Bibtex Download

@inbook{df0d8b5759164cfc9d56df302f39fa2d,
title = "Don{\textquoteright}t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records",
abstract = "Mental Health Records (MHRs) contain free- text documentation about patients{\textquoteright} 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",
author = "George Gkotsis and Sumithra Velupillai and Anika Oellrich and Harry Dean and Maria Liakata and Rina Dutta",
year = "2016",
month = jun,
day = "16",
language = "English",
pages = "95--105",
booktitle = "The Third Computational Linguistics and Clinical Psychology Workshop (CLPsych)",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Don’t Let Notes Be Misunderstood

T2 - A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records

AU - Gkotsis, George

AU - Velupillai, Sumithra

AU - Oellrich, Anika

AU - Dean, Harry

AU - Liakata, Maria

AU - Dutta, Rina

PY - 2016/6/16

Y1 - 2016/6/16

N2 - 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

AB - 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

UR - http://clpsych.org/

M3 - Conference paper

SP - 95

EP - 105

BT - The Third Computational Linguistics and Clinical Psychology Workshop (CLPsych)

ER -

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