{"title":"基于问题的计算语言方法在区分焦虑和抑郁方面优于评分量表","authors":"Mona Tabesh , Mariam Mirström , Rebecca Astrid Böhme , Marta Lasota , Yousef Javaherian , Thibaud Agbotsoka-Guiter , Sverker Sikström","doi":"10.1016/j.janxdis.2025.103020","DOIUrl":null,"url":null,"abstract":"<div><div>Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) and machine learning (ML) have opened up the possibility of question-based computational language assessment (QCLA). Here we investigate how accurate open-ended questions, using descriptive keywords or autobiographical narratives, can discriminate between participants that self-reported diagnosis of depression and anxiety, or health control. The results show that both language and rating scale measures can discriminate well, however, autobiographical narratives discriminate best between healthy and anxiety (ϕ = 1.58), as well as healthy and depression (ϕ = 1.38). Descriptive keywords, and to a certain extent autobiographical narratives, also discriminate better than summed scores of GAD-7 and PHQ-9 (ϕ=0.80 in discrimination between anxiety and depression), but not when individual items of these scales were analyzed by ML (ϕ=0.86 and ϕ=0.91 in item-level analysis of PHQ-9 and GAD-7, respectively). Combining the scales consistently elevated the discrimination even more (ϕ=1.39 in comparison between depression and anxiety), both in item-level and sum-scores analyses. These results indicate that QCLA measures often, but not in all cases, are better than standardized rating scales for assessment of depression and anxiety. Implication of these findings for mental health assessments are discussed.</div></div>","PeriodicalId":48390,"journal":{"name":"Journal of Anxiety Disorders","volume":"112 ","pages":"Article 103020"},"PeriodicalIF":4.8000,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression\",\"authors\":\"Mona Tabesh , Mariam Mirström , Rebecca Astrid Böhme , Marta Lasota , Yousef Javaherian , Thibaud Agbotsoka-Guiter , Sverker Sikström\",\"doi\":\"10.1016/j.janxdis.2025.103020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) and machine learning (ML) have opened up the possibility of question-based computational language assessment (QCLA). Here we investigate how accurate open-ended questions, using descriptive keywords or autobiographical narratives, can discriminate between participants that self-reported diagnosis of depression and anxiety, or health control. The results show that both language and rating scale measures can discriminate well, however, autobiographical narratives discriminate best between healthy and anxiety (ϕ = 1.58), as well as healthy and depression (ϕ = 1.38). Descriptive keywords, and to a certain extent autobiographical narratives, also discriminate better than summed scores of GAD-7 and PHQ-9 (ϕ=0.80 in discrimination between anxiety and depression), but not when individual items of these scales were analyzed by ML (ϕ=0.86 and ϕ=0.91 in item-level analysis of PHQ-9 and GAD-7, respectively). Combining the scales consistently elevated the discrimination even more (ϕ=1.39 in comparison between depression and anxiety), both in item-level and sum-scores analyses. These results indicate that QCLA measures often, but not in all cases, are better than standardized rating scales for assessment of depression and anxiety. Implication of these findings for mental health assessments are discussed.</div></div>\",\"PeriodicalId\":48390,\"journal\":{\"name\":\"Journal of Anxiety Disorders\",\"volume\":\"112 \",\"pages\":\"Article 103020\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Anxiety Disorders\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0887618525000568\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Anxiety Disorders","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0887618525000568","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression
Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) and machine learning (ML) have opened up the possibility of question-based computational language assessment (QCLA). Here we investigate how accurate open-ended questions, using descriptive keywords or autobiographical narratives, can discriminate between participants that self-reported diagnosis of depression and anxiety, or health control. The results show that both language and rating scale measures can discriminate well, however, autobiographical narratives discriminate best between healthy and anxiety (ϕ = 1.58), as well as healthy and depression (ϕ = 1.38). Descriptive keywords, and to a certain extent autobiographical narratives, also discriminate better than summed scores of GAD-7 and PHQ-9 (ϕ=0.80 in discrimination between anxiety and depression), but not when individual items of these scales were analyzed by ML (ϕ=0.86 and ϕ=0.91 in item-level analysis of PHQ-9 and GAD-7, respectively). Combining the scales consistently elevated the discrimination even more (ϕ=1.39 in comparison between depression and anxiety), both in item-level and sum-scores analyses. These results indicate that QCLA measures often, but not in all cases, are better than standardized rating scales for assessment of depression and anxiety. Implication of these findings for mental health assessments are discussed.
期刊介绍:
The Journal of Anxiety Disorders is an interdisciplinary journal that publishes research papers on all aspects of anxiety disorders for individuals of all age groups, including children, adolescents, adults, and the elderly. Manuscripts that focus on disorders previously classified as anxiety disorders such as obsessive-compulsive disorder and posttraumatic stress disorder, as well as the new category of illness anxiety disorder, are also within the scope of the journal. The research areas of focus include traditional, behavioral, cognitive, and biological assessment; diagnosis and classification; psychosocial and psychopharmacological treatment; genetics; epidemiology; and prevention. The journal welcomes theoretical and review articles that significantly contribute to current knowledge in the field. It is abstracted and indexed in various databases such as Elsevier, BIOBASE, PubMed/Medline, PsycINFO, BIOSIS Citation Index, BRS Data, Current Contents - Social & Behavioral Sciences, Pascal Francis, Scopus, and Google Scholar.