Toward Suicidal Ideation Detection with Lexical Network Features and Machine Learning

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dc.contributor.authorBayram, Ulya
dc.contributor.authorLee, William
dc.contributor.authorSantel, Daniel
dc.contributor.authorMinai, Ali A.
dc.contributor.authorClark, Peggy O.
dc.contributor.authorGlauser, Tracy
dc.contributor.authorPestian, John
dc.date.accessioned2025-01-27T18:58:59Z
dc.date.available2025-01-27T18:58:59Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIn this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection con-sists of interviews conducted by experts with epilepsy patients, with a few of them admitting to experiencing suicidal ideation in the past (32 suicidal and 77 control). The selected methods detect suicidal ideation with an average area under the curve (AUC) score of 95% on the merged collection with high suicidal ideation, and the trained models generalize over the third collection with an average AUC score of 69%. Results reveal that lexical networks are promising for classification and feature extraction as successful as the deep learning model. We also observe that a logistic classifier’s performance was comparable with the deep learning method while promising explainability. © 2022, Binghamton University Libraries. All rights reserved.
dc.identifier.doi10.22191/nejcs/vol4/iss1/2
dc.identifier.issn2577-8439
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85144518355
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.22191/nejcs/vol4/iss1/2
dc.identifier.urihttps://hdl.handle.net/20.500.12428/13105
dc.identifier.volume4
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherBinghamton University Libraries
dc.relation.ispartofNortheast Journal of Complex Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20250125
dc.titleToward Suicidal Ideation Detection with Lexical Network Features and Machine Learning
dc.typeArticle

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