AIMC Topic: Suicide Prevention

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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
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...

Deep neural networks detect suicide risk from textual facebook posts.

Scientific reports
Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56-0.58). In this study, Artificial Neural Network (ANN) models were constructed to pred...

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.

International journal of environmental research and public health
Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A system...

Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

The Journal of clinical psychiatry
OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' perceptions of suicide risk flags generated by these algorithms, which may affect algorithm implement...

Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

Psychological medicine
BACKGROUND: This study evaluated whether natural language processing (NLP) of psychotherapy note text provides additional accuracy over and above currently used suicide prediction models.

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors.

The Australian and New Zealand journal of psychiatry
OBJECTIVE: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction effo...

Machine learning methods for developing precision treatment rules with observational data.

Behaviour research and therapy
Clinical trials have identified a variety of predictor variables for use in precision treatment protocols, ranging from clinical biomarkers and symptom profiles to self-report measures of various sorts. Although such variables are informative collect...

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

IEEE journal of biomedical and health informatics
This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asy...

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.

Journal of medical Internet research
BACKGROUND: Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motiva...