Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Objective: We designed an artificial intelligence approach to identify dissociative patients and predict prior suicide attempts in an unbiased, data-driven manner.Method: Participants were 30 controls and 93 treatment-seeking female patients with posttraumatic stress disorder (PTSD) and various levels of dissociation, including some with the PTSD dissociative subtype and some with dissociative identity disorder (DID).Results: Unsupervised learning models identified patients along a spectrum of dissociation. Moreover, supervised learning models accurately predicted prior suicide attempts with an F1 score up to 0.83. DID had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in PTSD and DID.Conclusions: These findings expand our understanding of the dissociative phenotype and underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.
Antecedentes: El suicidio es una de las causas principales de muerte y las tasas de intentos suicidas han aumentado durante la pandemia de COVID-19. El fenotipo psiquiátrico sub-diagnosticado de disociación se asocia con autolesiones suicidas elevadas; sin embargo, en gran medida se ha dejado fuera de los intentos para predecir y prevenir el suicidio.
Objetivo: Diseñamos un enfoque de una inteligencia artificial para identificar a los pacientes con trastornos disociativos y predecir intentos suicidas previos en una forma imparcial y basada en datos.
Método: Los participantes fueron 30 mujeres controles y 93 pacientes con trastorno de estrés postraumático (TEPT) y niveles variados de disociación que buscaban tratamiento, incluyendo algunas con TEPT subtipo disociativo y algunas con trastorno de la identidad disociativo (TID).
Resultados: Los modelos de aprendizaje no supervisados identificaron pacientes a lo largo de un espectro de disociación. Además, los modelos de aprendizaje supervisados predijeron con precisión los intentos suicidas previos con una puntuación de hasta 0.83. El TID tuvo el riesgo más alto de intentos suicidas previos y los distintos subtipos de disociación predijeron intentos suicidas en TEPT y TID.
Conclusiones: Estos hallazgos expanden nuestra comprensión del fenotipo disociativo y subrayan la necesidad urgente de evaluar la disociación para identificar a las personas con alto riesgo de autolesiones suicidas.
背景：自杀是导致死亡的主要原因，自杀未遂率在 COVID-19 疫情期间有所增加。未得到充分诊断的精神病学解离表型与自杀性自伤的升高有关；然而，它在很大程度上被排除在预测和预防自杀的尝试之外。
目的：我们设计了一种人工智能方法来识别解离患者并以无偏见的数据驱动方式预测先前的自杀意图。
方法：参与者是 30 名对照和 93 名寻求治疗的患有创伤后应激障碍 (PTSD) 和不同程度解离的女性患者，包括一些患有 PTSD 解离亚型和一些患有解离性身份障碍 (DID) 的女性。
结果：无监督学习模型识别出一系列解离患者。此外，监督学习模型准确地预测了先前的自杀意图，得分高达 0.83。 DID 具有最高的先前自杀意图的风险，并且不同的解离亚型预测了 PTSD 和 DID 的自杀意图。
结论：这些发现拓展了我们对解离表型的理解，并强调了评估解离以识别具有自杀性自伤高风险个体的迫切需要。
Keywords: Autolesiones suicidas; Suicidal self-injury; aprendizaje automático; artificial intelligence; disociación; dissociation; dissociative identity disorder; inteligencia artificial; machine learning; posttraumatic stress disorder; suicide; suicidio; trastorno de estrés postraumático; trastorno de la identidad disociativo; 人工智能; 创伤后应激障碍; 机器学习; 自杀; 自杀性自伤; 解离; 解离性身份障碍.
Dissociation, feelings of detachment and disruption in one's sense of self and surroundings, is associated with an elevated risk of suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Using machine learning techniques, we found dissociative identity disorder had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in posttraumatic stress disorder and dissociative identity disorder.These findings underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.