AIMC Topic: Suicide, Attempted
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User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, w...
Selection of Clinical Text Features for Classifying Suicide Attempts.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electronic health record (EHR) data are being explored to better identify patients, a process called EHR ph...
Development of a Self-Harm Monitoring System for Victoria.
International journal of environmental research and public health
Dec 15, 2020
The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and internationally. The World Health Organization has recommended that member states develop self-harm surveillance systems as part of their suicide preve...
Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data.
PLoS medicine
Nov 6, 2020
BACKGROUND: Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for predict...
Using Machine Learning to Predict Suicide Attempts in Military Personnel.
Psychiatry research
Oct 22, 2020
Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the low base rate and underpowered statistical approaches. The objective of the current study was to use...
Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.
BMC psychiatry
Mar 30, 2020
BACKGROUND: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional depend...
Using machine learning to classify suicide attempt history among youth in medical care settings.
Journal of affective disorders
Feb 28, 2020
BACKGROUND: The current study aimed to classify recent and lifetime suicide attempt history among youth presenting to medical settings using machine learning (ML) as applied to a behavioral health screen self-report survey.
Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.
Translational psychiatry
Feb 20, 2020
Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to th...
Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning.
Journal of affective disorders
Nov 13, 2019
BACKGROUND: The use of machine learning (ML) algorithms to study suicidality has recently been recommended. Our aim was to explore whether ML approaches have the potential to improve the prediction of suicide attempt (SA) risk. Using the epidemiologi...
Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol.
BMC psychiatry
Sep 7, 2019
BACKGROUND: The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information's for patient management. Artificial intelligence (AI) techniques allow processing o...
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