Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.
Background: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we i...
| Publicado en: | Psychological Medicine Vol. 52; no. 5; pp. 957 - 968 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Cambridge University Press
Apr2022
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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=ccm&AN=156193987&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156193987 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00332917 6Q3 jtl: Psychological Medicine issn: 00332917 maglogo: N pubinfo: dt: Apr2022 vid: 52 iid: 5 pid: 15979 pub: Cambridge University Press artinfo: ui: 156193987 156193987 156193987 10.1017/S0033291720002718 156193987 ppf: 957 ppct: 11 formats: tig: atl: Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood. aug: au: Schultebraucks, Katharina Yadav, Vijay Shalev, Arieh Y. Bonanno, George A. Galatzer-Levy, Isaac R. affil: Department of Emergency Medicine, Vagelos School of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA sug: subj: Deep Learning Stress Disorders, Post-Traumatic Depression Trauma Machine Learning Semantics Human Semantic Analysis Emergency Service Patient Admission Interviews Emotions Speech ab: Background: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). Methods: N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally. Results: Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82). Conclusions: Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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