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Decoding the adolescent non-suicidal self-injury

Understanding with interpretable machine learning insights

Bibliographic Data

ID15366237
AuthorsHaojie Fu (0000-0003-1547-7250, Tongji University, corresponding author), Mengmeng Zhang (0000-0002-1529-5038, Heidelberg University), Shuran Yang (0000-0001-9143-0900, Shanghai East Hospital), Chuanyuan Kang (0009-0007-7382-7649, Shanghai East Hospital), Liang Liu (0000-0003-1372-4596, Shanghai Mental Health Center), Xudong Zhao (0000-0003-1493-3517, Shanghai East Hospital)
Year2025
Volume25
Issue1
Pages2994-2994
Publication date2025-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBMC Public Health (JOURNAL)
Journal identifiersISSN: 1471-2458 • E-ISSN: 1471-2458
PublisherBioMed Central (PUBLISHER • GB)
DOI10.1186/s12889-025-24354-z
PMID40890657
OpenAlexW4413875573
LanguageEN
Citations received2
References cited93

Leveraging multiple machine learning algorithms for a holistic item analysis, this research identifies critical risk and protective factors for non-suicidal self-injury, thus refining the Integrated Theoretical Model

Biostatistics · Injury prevention · Medical emergency · Occupational safety and health · Pathology · Poison control · Public health · Suicide prevention · Human Factors and Ergonomics · Medicine · Neuroscience of respiration and sleep · Suicide and Self-Harm Studies · Traumatic Brain Injury Research · Epidemiology

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Unique citing works2
Citations per year2
Citation span2026 - 2026 (1)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 2

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