Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders
Langue
EN
Article de revue
Ce document a été publié dans
Scientific Reports. 2017-02-16, vol. 7
Résumé en anglais
Embodied Conversational Agents (ECAs) are promising software to communicate with patients but no study has tested them in the diagnostic field of mental disorders. The aim of this study was 1) to test the performance of a ...Lire la suite >
Embodied Conversational Agents (ECAs) are promising software to communicate with patients but no study has tested them in the diagnostic field of mental disorders. The aim of this study was 1) to test the performance of a diagnostic system for major depressive disorders (MDD), based on the identification by an ECA of specific symptoms (the MDD DSM 5 criteria) in outpatients; 2) to evaluate the acceptability of such an ECA. Patients completed two clinical interviews in a randomized order (ECA versus psychiatrist) and filled in the Acceptability E-scale (AES) to quantify the acceptability of the ECA. 179 outpatients were included in this study (mean age 46.5 ± 12.9 years, 57.5% females). Among the 35 patients diagnosed with MDD by the psychiatrist, 14 (40%) patients exhibited mild, 12 (34.3%) moderate and 9 (25.7%) severe depressive symptoms. Sensitivity increased across the severity level of depressive symptoms and reached 73% for patients with severe depressive symptoms, while specificity remained above 95% for all three severity levels. The acceptability of the ECA evaluated by the AES was very good (25.4). We demonstrate here the validity and acceptability of an ECA to diagnose major depressive disorders. ECAs are promising tools to conduct standardized and well-accepted clinical interviews.< Réduire
Mots clés en anglais
Adult
Computer interface
Diagnostic and statistical manual of mental disorders
Female
Human
Major depression
Male
Middle aged
Patient attitude
Proof of concept
Psychological interview
Psychology
Receiver operating characteristic
Reproducibility
Severity of illness index
Software
Project ANR
Phénotypage humain et réalité virtuelle - ANR-10-EQPX-0012
Unités de recherche