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Machine Learning-Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study.

Hyejun KimYejun SonHojae LeeJiseung KangAhmed HammoodiYujin ChoiHyeon Jin KimHayeon LeeGuillaume FondLaurent BoyerRosie KwonSelin WooDong-Keon Yon
Published in: Journal of medical Internet research (2024)
This study used ML by integrating diverse data sets from 3 countries to address adolescent suicide. The findings highlight the important role of emotional health indicators in predicting suicidal thinking among adolescents. Specifically, sadness and despair were identified as the most significant predictors, followed by stressful conditions and age. These findings emphasize the critical need for early diagnosis and prevention of mental health issues during adolescence.
Keyphrases
  • mental health
  • machine learning
  • depressive symptoms
  • young adults
  • public health
  • healthcare
  • deep learning
  • artificial intelligence
  • electronic health record
  • neural network
  • data analysis