AIMC Topic: Suicide, Attempted
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Development and Validation of a Prediction Model for Co-Occurring Moderate-to-Severe Anxiety Symptoms in First-Episode and Drug Naïve Patients With Major Depressive Disorder.
Depression and anxiety
Nov 19, 2024
Moderate-to-severe anxiety symptoms are severe and common in patients with major depressive disorder (MDD) and have a significant impact on MDD patients and their families. The main objective of this study was to develop a risk prediction model for ...
Prediction of Suicidal Thoughts and Suicide Attempts in People Who Gamble Based on Biological-Psychological-Social Variables: A Machine Learning Study.
The Psychiatric quarterly
Oct 28, 2024
Recent research has shown that people who gamble are more likely to have suicidal thoughts and attempts compared to the general population. Despite the advancements made, no study to date has predicted suicide risk factors in people who gamble using ...
Suicidal behaviors among high school graduates with preexisting mental health problems: A machine learning and GIS-based study.
The International journal of social psychiatry
Sep 5, 2024
BACKGROUND: Suicidal behavior among adolescents with mental health disorders, such as depression and anxiety, is a critical issue. This study explores the prevalence and predictors of past-year suicidal behaviors among Bangladeshi high school graduat...
Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence.
Journal of affective disorders
Aug 12, 2024
BACKGROUND: Prevalence of suicidal behaviour increases rapidly in middle to late adolescence. Predicting suicide attempts across different ages would enhance our understanding of how suicidal behaviour manifests in this period of rapid development. T...
Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.
Translational psychiatry
Jul 31, 2024
Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is ...
Anticipating influential factors on suicide outcomes through machine learning techniques: Insights from a suicide registration program in western Iran.
Asian journal of psychiatry
Jul 28, 2024
Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influen...
Constructing prediction models and analyzing factors in suicidal ideation using machine learning, focusing on the older population.
PloS one
Jul 22, 2024
Suicide among the older population is a significant public health concern in South Korea. As the older individuals have long considered suicide before committing suicide trials, it is important to analyze the suicidal ideation that precedes the suici...
Development and external validation of a logistic and a penalized logistic model using machine-learning techniques to predict suicide attempts: A multicenter prospective cohort study in Korea.
Journal of psychiatric research
Jun 6, 2024
Despite previous efforts to build statistical models for predicting the risk of suicidal behavior using machine-learning analysis, a high-accuracy model can lead to overfitting. Furthermore, internal validation cannot completely address this problem....
Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of : A Pilot Integrative Machine Learning Study.
Depression and anxiety
May 9, 2024
Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide predicti...
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