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Unravelling psychiatric heterogeneity and predicting suicide attempts in women with trauma-related dissociation using artificial intelligence.
Srinivasan, Suhas; Harnett, Nathaniel G; Zhang, Liang; Dahlgren, M Kathryn; Jang, Junbong; Lu, Senbao; Nephew, Benjamin C; Palermo, Cori A; Pan, Xi; Eltabakh, Mohamed Y; Frederick, Blaise B; Gruber, Staci A; Kaufman, Milissa L; King, Jean; Ressler, Kerry J; Winternitz, Sherry; Korkin, Dmitry; Lebois, Lauren A M.
Afiliación
  • Srinivasan S; Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Harnett NG; Department of Dermatology, Stanford School of Medicine, Stanford, CA, USA.
  • Zhang L; McLean Hospital, Belmont, MA, USA.
  • Dahlgren MK; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
  • Jang J; Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Lu S; McLean Hospital, Belmont, MA, USA.
  • Nephew BC; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
  • Palermo CA; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Pan X; Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Eltabakh MY; Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Frederick BB; Department of Neuroscience, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Gruber SA; McLean Hospital, Belmont, MA, USA.
  • Kaufman ML; McLean Hospital, Belmont, MA, USA.
  • King J; Data Science Program, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Ressler KJ; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Winternitz S; Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA.
  • Korkin D; McLean Hospital, Belmont, MA, USA.
  • Lebois LAM; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Eur J Psychotraumatol ; 13(2): 2143693, 2022 Dec.
Article en En | MEDLINE | ID: mdl-38872600
ABSTRACT

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.
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Eur J Psychotraumatol Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Eur J Psychotraumatol Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos