ArticleJournal of medical Internet research2021
Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric Inpatients: Proof-of-Concept Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 24 citations in OpenAlex.
- Using Automated Coding of Nonverbal Behavior During a Suicide Assessment to Inform Risk Detection: Mixed Cross-Sectional and Exploratory Prospective Study.JMIR formative research · 2026Article
- Observational
- Automated detection and prediction of suicidal behavior from clinical notes using deep learning.PloS one · 2025Article
- Three-dimensional facial features of suicide risk in females with depression.Frontiers in psychiatry · 2025Article
- Article
- The use of advanced technology and statistical methods to predict and prevent suicide.Nature reviews psychology · 2023Article
- A multimodal dialog approach to mental state characterization in clinically depressed, anxious, and suicidal populations.Frontiers in psychology · 2023Article
- Detection of Suicide Risk Using Vocal Characteristics: Systematic Review.JMIR biomedical engineering · 2022Review
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Choices of (in)action in obesity: Implications for research on treatment and prevention.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple 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.
objectiveWe 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.
methodsWe 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.
resultsSuicide severity was associated with multiple visual and auditory markers, including speech prevalence (β=-0.68, P=.02, r
conclusionsDigital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.