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Deep graph neural network-based prediction of acute suicidal ideation in young adults.
Choi, Kyu Sung; Kim, Sunghwan; Kim, Byung-Hoon; Jeon, Hong Jin; Kim, Jong-Hoon; Jang, Joon Hwan; Jeong, Bumseok.
  • Choi KS; Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
  • Kim S; Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
  • Kim BH; Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Jeon HJ; Department of Bio and Brain Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Kim JH; Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Jang JH; Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Gachon University, Incheon, Republic of Korea.
  • Jeong B; Neuroscience Research Institute, Gachon Advanced Institute for Health Science and Technology, Gachon University, Incheon, Republic of Korea.
Sci Rep ; 11(1): 15828, 2021 08 04.
Article in English | MEDLINE | ID: covidwho-1343475
ABSTRACT
Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. The best model achieved a sensitivity of 76.3%, specificity of 83.4%, and an area under curve of 0.878 (95% confidence interval, 0.855-0.899). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to the prediction of suicidal ideation. Our model might be useful for the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Suicide, Attempted / Neural Networks, Computer / Suicidal Ideation / Mental Disorders Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study / Qualitative research / Randomized controlled trials Limits: Adolescent / Adult / Female / Humans / Male / Young adult Country/Region as subject: Asia Language: English Journal: Sci Rep Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Suicide, Attempted / Neural Networks, Computer / Suicidal Ideation / Mental Disorders Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study / Qualitative research / Randomized controlled trials Limits: Adolescent / Adult / Female / Humans / Male / Young adult Country/Region as subject: Asia Language: English Journal: Sci Rep Year: 2021 Document Type: Article