.png){kind=logo}
Predicting and preventing suicide, by identifying early high risk patients.
In Latin America, high levels of depression and suicidal thoughts represent an increasing public health threat.
The Pan American Health Organization (PAHO) reported that 9% of healthcare workers in the region experience suicidal thoughts, reflecting an alarming trend toward suicide risk [1].
Early detection is critical, but traditional diagnostic methods often fail to identify the most vulnerable patients intime.
Suicide rates in Latin America are rising, reaching concerning figures. In 2020, the suicide rate in the region was 5.1 per-100,000 inhabitants, with significant increases in countries such as Brazil and Mexico, which recorded increases of 3.5% and 2.9%, respectively [2].
The proposed predictive model uses synthetic data encompassing clinical and behavioral variables such as mental health history, standardized questionnaire scores (PHQ-9 and GAD-7), and behaviors associated with higher suicide risk.
The model can identify patients at high risk early, enabling timely and personalized interventions.
The variables for the predictive model are drawn from different research, including a systematic review by Lejeune et al., a study on machine learning in suicide prediction by Kumar et al., and literature on AI strategies for assessing suicidal behavior by Khan and Javed. Furthermore, it incorporates the potential of AI to predict suicide risk, as highlighted by Parsapoor et al. and the future prospects of AI in this field, providing the model with a solid foundation for accurate prediction.
1. Pan American Health Organization (PAHO). (2022, January 13). Study warns of high levels of depression and suicidal thoughts in healthcare workers. Retrieved from https://www.paho.org/es/noticias/13-1-2022-estudio-advierte-sobre-elevados-niveles-depresion-pensamientos-suicidas-personal
3. Predictive Assessment of Suicide Risk: Clinical Implications. Frontiers in Psychiatry. https://doi.org/10.3389/fpsyt.2023.1186569
4. DW. (2023, June 22). Latin America on alert for rising suicide rates. Retrieved from https://www.dw.com/es/am%C3%A9rica-latina-en-alerta-por-aumento-en-tasas-de-suicidio/a-65493663
5. Nature. (2022). Advances in Predictive Modeling of Suicide Risk Using Behavioral Data. Nature. Retrieved from https://www.nature.com/articles/s44184-022-00002-x
6. PMC. (2021). Predicting Suicide Risk: A Review of Predictive Models and Their Application. PMC. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC8988272/
In Latin America, high levels of depression and suicidal thoughts represent an increasing public health threat.
The Pan American Health Organization (PAHO) reported that 9% of healthcare workers in the region experience suicidal thoughts, reflecting an alarming trend toward suicide risk [1].
Early detection is critical, but traditional diagnostic methods often fail to identify the most vulnerable patients intime.
Suicide rates in Latin America are rising, reaching concerning figures. In 2020, the suicide rate in the region was 5.1 per-100,000 inhabitants, with significant increases in countries such as Brazil and Mexico, which recorded increases of 3.5% and 2.9%, respectively [2].
The proposed predictive model uses synthetic data encompassing clinical and behavioral variables such as mental health history, standardized questionnaire scores (PHQ-9 and GAD-7), and behaviors associated with higher suicide risk.
The model can identify patients at high risk early, enabling timely and personalized interventions.
Considering that the suicide rate in Latin America is 5.1 per-100,000 inhabitants [2] and the region’s population exceeds 650 million, the estimated annual cost of suicide could surpass $45 million USD.
Additionally, studies show that suicide prevention through early risk identification can reduce suicide attempts by up to 30% [3].
According to PAHO (2022), each suicide in the region generates an estimated cost of $7,000 USD for emergency medical care, hospitalization, and long-term treatment of suicide attempt survivors.
The implementation of this predictive model could reduce suicide rates by enabling timely interventions. According to a study in Nature (2022), the use of AI technologies for predicting suicide risk has improved intervention accuracy by 30%. This not only enhances patient outcomes but also alleviates pressure on healthcare systems by optimizing resources and reducing long-term costs.
The variables for the predictive model are drawn from different research, including a systematic review by Lejeune et al., a study on machine learning in suicide prediction by Kumar et al., and literature on AI strategies for assessing suicidal behavior by Khan and Javed. Furthermore, it incorporates the potential of AI to predict suicide risk, as highlighted by Parsapoor et al. and the future prospects of AI in this field, providing the model with a solid foundation for accurate prediction.
1. Pan American Health Organization (PAHO). (2022, January 13). Study warns of high levels of depression and suicidal thoughts in healthcare workers. Retrieved from https://www.paho.org/es/noticias/13-1-2022-estudio-advierte-sobre-elevados-niveles-depresion-pensamientos-suicidas-personal
3. Predictive Assessment of Suicide Risk: Clinical Implications. Frontiers in Psychiatry. https://doi.org/10.3389/fpsyt.2023.1186569
4. DW. (2023, June 22). Latin America on alert for rising suicide rates. Retrieved from https://www.dw.com/es/am%C3%A9rica-latina-en-alerta-por-aumento-en-tasas-de-suicidio/a-65493663
5. Nature. (2022). Advances in Predictive Modeling of Suicide Risk Using Behavioral Data. Nature. Retrieved from https://www.nature.com/articles/s44184-022-00002-x
6. PMC. (2021). Predicting Suicide Risk: A Review of Predictive Models and Their Application. PMC. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC8988272/
