ArticleNPP - digital psychiatry and neuroscience2024
Neural activity during inhibitory control predicts suicidal ideation with machine learning.
Article in NPP - digital psychiatry and neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study.Lancet regional health. Americas · 2026Article
- Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study.NPP - digital psychiatry and neuroscience · 2026Article
- Brief mindfulness coaching enhances selective attention in medical scientists: A pilot study.PloS one · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suicide is a leading cause of death in the US and worldwide. Current strategies for preventing suicide are often focused on the identification and treatment of risk factors, especially suicidal ideation (SI). Hence, developing data-driven biomarkers of SI may be key for suicide prevention and intervention. Prior attempts at biomarker-based prediction models for SI have primarily used expensive neuroimaging technologies, yet clinically scalable and affordable biomarkers remain elusive. Here, we investigated the classification of SI using machine learning (ML) on a dataset of 76 subjects with and without SI(+/-) (
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Registered trials
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