Shortening and Personalizing Psychodiagnostic Assessments with Decision Tree-Machine Learning Classifiers: An Application Example Based on the Patient Health Questionnaire-9.
The development of psychological assessment tools that accurately and efficiently classify individuals as having or not a specific diagnosis is a major challenge for test developers and mental health professionals. This paper shows how machine learning (ML) provides a valuable framework to improve t...
| Publicado en: | International Journal of Mental Health & Addiction Vol. 24; no. 2; pp. 1004 - 1025 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193492994&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193492994 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 15571874 46AW jtl: International Journal of Mental Health & Addiction issn: 15571874 maglogo: N pubinfo: dt: Apr2026 vid: 24 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 193492994 10.1007/s11469-024-01332-x ppf: 1004 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: Shortening and Personalizing Psychodiagnostic Assessments with Decision Tree-Machine Learning Classifiers: An Application Example Based on the Patient Health Questionnaire-9. aug: au: Colledani, Daiana Robusto, Egidio Anselmi, Pasquale affil: https://ror.org/00240q980 Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Via Venezia 14, 35131, Padua, Italy https://ror.org/02be6w209 Department of Psychology, Faculty of Medicine and Psychology, Sapienza University of Rome, Via Dei Marsi 78, 00185, Rome, Italy su: Psychological tests Decision trees Machine learning Classification Health surveys Feature selection Psychodiagnostics sug: subj: Psychological tests Decision trees Machine learning Classification Health surveys Feature selection Psychodiagnostics keyword: Decision tree Machine learning classifiers Patient Health Questionnaire-9 Psychological assessment Psychology and Cognitive Sciences Psychology Receiver operating characteristic curve Decision tree Machine learning classifiers Patient Health Questionnaire-9 Psychological assessment Psychology and Cognitive Sciences Psychology Receiver operating characteristic curve ab: The development of psychological assessment tools that accurately and efficiently classify individuals as having or not a specific diagnosis is a major challenge for test developers and mental health professionals. This paper shows how machine learning (ML) provides a valuable framework to improve the accuracy and efficiency of psychodiagnostic classifications. The method is illustrated using an empirical example based on the Patient Health Questionnaire-9 (PHQ-9). The results show that, compared to traditional scorings of the PHQ-9, that based on decision tree (DT) algorithms is more advantageous in terms of accuracy and efficiency. In addition, the DT-based method facilitates the development of short test forms and improves the diagnostic performance of the test by integrating external information (e.g., demographic variables) into the scoring process. These findings suggest that DT-algorithms and ML applications such as feature selection represent a valuable method for supporting test developers and mental health professionals, and highlight the potential of ML for advancing the field of psychological assessment. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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