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Predicting and preventing suicide, particularly through the use of machine learning algorithms.
Suicide remains a critical issue in the United States, currently standing as the 10th leading cause of death, signaling a pervasive yet often preventable public health crisis (1)(2). Each day sees an average of 16.8 individuals succumbing to suicide, pointing to the urgent need for effective intervention strategies (3). Despite the common perception of suicide occurring without warning, evidence suggests that 90% of those who die by suicide have a treatable mental health condition (4). The challenge lies in shifting from a predominantly reactive approach to a proactive one, where early detection and support systems for mental health can intervene long before individuals reach a point of crisis. Increasing the focus on prevention and timely care is imperative in reducing the incidence of suicide and enhancing mental health support across communities (5).
To address this need, we have developed a predictive analytics model, “SuicideRisk AI,” that leverages synthetic data spanning a multitude of clinical and behavioral variables, such as mental health history, scores from standard questionnaires such as PHQ-9, and GAD- 7, and behaviors related to increased risk of suicide. This tool helps doctors in the early detection of patients at high risk of suicide.
The variables for the predictive model are drawn from different research, including a systematic review by Lejeune et al. (1), a study on machine learning in suicide prediction by Kumar et al. (2), and literature on AI strategies for assessing suicidal behavior by Khan and Javed (3). Furthermore, it incorporates the potential of AI to predict suicide risk, as highlighted by Parsapoor et al. (4) and the future prospects of AI in this field (5), providing the model with a solid foundation for accurate prediction.
Suicide remains a critical issue in the United States, currently standing as the 10th leading cause of death, signaling a pervasive yet often preventable public health crisis (1)(2). Each day sees an average of 16.8 individuals succumbing to suicide, pointing to the urgent need for effective intervention strategies (3). Despite the common perception of suicide occurring without warning, evidence suggests that 90% of those who die by suicide have a treatable mental health condition (4). The challenge lies in shifting from a predominantly reactive approach to a proactive one, where early detection and support systems for mental health can intervene long before individuals reach a point of crisis. Increasing the focus on prevention and timely care is imperative in reducing the incidence of suicide and enhancing mental health support across communities (5).
To address this need, we have developed a predictive analytics model, “SuicideRisk AI,” that leverages synthetic data spanning a multitude of clinical and behavioral variables, such as mental health history, scores from standard questionnaires such as PHQ-9, and GAD- 7, and behaviors related to increased risk of suicide. This tool helps doctors in the early detection of patients at high risk of suicide.
The variables for the predictive model are drawn from different research, including a systematic review by Lejeune et al. (1), a study on machine learning in suicide prediction by Kumar et al. (2), and literature on AI strategies for assessing suicidal behavior by Khan and Javed (3). Furthermore, it incorporates the potential of AI to predict suicide risk, as highlighted by Parsapoor et al. (4) and the future prospects of AI in this field (5), providing the model with a solid foundation for accurate prediction.
