AIMC Topic: Suicide, Attempted

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Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing.

BMC medical informatics and decision making
BACKGROUND: We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening suicidal behavior among pregnant women in electronic medical records (EMRs).

Emotional hyper-reactivity and cardiometabolic risk in remitted bipolar patients: a machine learning approach.

Acta psychiatrica Scandinavica
OBJECTIVE: Remitted bipolar disorder (BD) patients frequently present with chronic mood instability and emotional hyper-reactivity, associated with poor psychosocial functioning and low-grade inflammation. We investigated emotional hyper-reactivity a...

Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.

Scientific reports
Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information ext...

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be time consuming to implement at scale, if it is done at all. Computational algorithms may predict suic...

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

General hospital psychiatry
OBJECTIVE: Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integratio...

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.

Journal of affective disorders
OBJECTIVE: A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to...

A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department.

Suicide & life-threatening behavior
What adolescents say when they think about or attempt suicide influences the medical care they receive. Mental health professionals use teenagers' words, actions, and gestures to gain insight into their emotional state and to prescribe what they beli...

Using machine learning approach to predict suicide ideation and suicide attempts among Chinese adolescents: A cross-sectional study.

Journal of affective disorders
BACKGROUND: Screening for suicide ideation and suicide attempts is crucial for adolescents, yet accurately predicting these outcomes remains a significant challenge. The relationship between non-suicidal self-injury and suicide ideation and attempts ...

Evaluating the ability of artificial intelligence to predict suicide: A systematic review of reviews.

Journal of affective disorders
INTRODUCTION: Suicide remains a critical global public health issue, with approximately 800,000 deaths annually. Despite various prevention efforts, suicide rates are rising, highlighting the need for more effective strategies. Traditional suicide ri...