Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric Inpatients: Proof-of-Concept Study
| Autor: | Isaac R. Galatzer-Levy, Erich Seifritz, Anja Ries, Stefan Vetter, Vidya Koesmahargyo, Michael Colla, Anzar Abbas, Stephanie Homan, Vijay Kumar Yadav, Laura Sels, Hanne Scheerer, Urte Scholz, Birgit Kleim |
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| Přispěvatelé: | University of Zurich, Koesmahargyo, Vidya |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Emotions
Social Sciences Suicide Attempted Audiology 0302 clinical medicine suicide risk Risk Factors digital biomarkers Medicine Psychiatric hospital 10064 Neuroscience Center Zurich Suicide Risk Suicidal ideation digital phenotyping Depression (differential diagnoses) 2718 Health Informatics RISK 10093 Institute of Psychology SEROTONIN DEPRESSION depression visual Public aspects of medicine RA1-1270 medicine.symptom medicine.medical_specialty Computer applications to medicine. Medical informatics R858-859.7 digital health Health Informatics POSTMORTEM BEHAVIORS Affect (psychology) 03 medical and health sciences Humans Expressivity (genetics) auditory suicide Inpatients Original Paper Suicide attempt business.industry digital 030227 psychiatry suicidal ideation THOUGHTS Mood 10054 Clinic for Psychiatry Psychotherapy and Psychosomatics digital markers business 150 Psychology 030217 neurology & neurosurgery facial |
| Zdroj: | Journal of Medical Internet Research JOURNAL OF MEDICAL INTERNET RESEARCH Journal of Medical Internet Research, Vol 23, Iss 6, p e25199 (2021) |
| ISSN: | 1438-8871 |
| Popis: | Background Multiple symptoms of suicide risk have been assessed based on visual and auditory information, including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. Objective We aimed to examine measurements extracted from video interviews using open-source deep learning algorithms to quantify facial, vocal, and movement behaviors in relation to suicide risk severity in recently admitted patients following a suicide attempt. Methods We utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open-source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide risk with the Beck Scale for Suicide Ideation controlling for age and sex, using multiple linear regression. Results Suicide severity was associated with multiple visual and auditory markers, including speech prevalence (β=−0.68, P=.02, r2=0.40), overall expressivity (β=−0.46, P=.10, r2=0.27), and head movement measured as head pitch variability (β=−1.24, P=.006, r2=0.48) and head yaw variability (β=−0.54, P=.06, r2=0.32). Conclusions Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation. |
| Databáze: | OpenAIRE |
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