AIMC Topic: Self-Injurious Behavior

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Development of an explainable machine learning model for predicting depression in adolescent girls with non-suicidal self-injury: A cross-sectional multicenter study.

Journal of affective disorders Jun 15, 2025
Non-suicidal self-injury (NSSI) in adolescent girls is a critical predictor of subsequent depression and suicide risk, yet current tools lack both accuracy and clinical interpretability. We developed the first explainable machine learning model integ...
Machine Learning Female Humans Depression Adolescent Self-Injurious Behavior Risk Factors Cross-Sectional Studies
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Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

Medical care Feb 1, 2021
BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increas...
Female Reproducibility of Results Retrospective Studies Electronic Health Records Aged Young Adult Risk Factors Hospitalization Humans Algorithms Mental Disorders Women Cohort Studies Middle Aged Supervised Machine Learning Adult Patient Readmission Suicide, Attempted Sensitivity and Specificity Self-Injurious Behavior
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Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.

Journal of autism and developmental disorders Nov 1, 2020
Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To support an SIB monitoring system for autism spectrum disorder (ASD), we evaluated machine learning me...
Self-Injurious Behavior Electrocardiography Cluster Analysis Child, Preschool Child Autism Spectrum Disorder Female Male Adolescent Humans Machine Learning
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Imputation and characterization of uncoded self-harm in major mental illness using machine learning.

Journal of the American Medical Informatics Association : JAMIA Jan 1, 2020
OBJECTIVE: We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm incidence, and identify factors associated with coding bias.
Suicidal Ideation Incidence Datasets as Topic Self-Injurious Behavior Classification Algorithms Adult Humans Mental Disorders Clinical Coding Male Machine Learning Electronic Health Records Female
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Machine Learning Models for Prediction of Reinjury After Penetrating Trauma.

JAMA surgery Feb 1, 2018
This study compares several machine learning models for prediction of reinjury after penetrating trauma.
Recurrence Databases, Factual Wounds, Penetrating Suicide Forecasting Humans Patient Readmission Mental Disorders Self-Injurious Behavior Machine Learning United States Models, Theoretical
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