Your browser doesn't support javascript.
loading
Machine learning-based prediction of suicidality in adolescents during the COVID-19 pandemic (2020-2021): Derivation and validation in two independent nationwide cohorts.
Kwon, Rosie; Lee, Hayeon; Kim, Min Seo; Lee, Jinseok; Yon, Dong Keon.
Affiliation
  • Kwon R; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Department of Regulatory Science, Kyung Hee University, Seoul, South Korea.
  • Lee H; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Department of Biomedical Engineering, Kyung Hee University, Yongin, South Korea.
  • Kim MS; Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Lee J; Department of Biomedical Engineering, Kyung Hee University, Yongin, South Korea. Electronic address: gonasago@khu.ac.kr.
  • Yon DK; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Department of Regulatory Science, Kyung Hee University, Seoul, South Korea; Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medi
Asian J Psychiatr ; 88: 103704, 2023 10.
Article in En | MEDLINE | ID: mdl-37541104
Subject(s)
Key words
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Suicide / COVID-19 Type of study: Prognostic_studies / Risk_factors_studies Limits: Adolescent / Humans Language: En Journal: Asian J Psychiatr Year: 2023 Document type: Article Affiliation country: South Korea Country of publication: Netherlands
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Suicide / COVID-19 Type of study: Prognostic_studies / Risk_factors_studies Limits: Adolescent / Humans Language: En Journal: Asian J Psychiatr Year: 2023 Document type: Article Affiliation country: South Korea Country of publication: Netherlands