Can machine learning identify childhood characteristics that predict future development of bipolar disorder a decade later?

ABSTRACT: Early identification of bipolar disorder may provide appropriate support and treatment, however there is no current evidence for statistically predicting whether a child will develop bipolar disorder. Machine learning methods offer an opportunity for developing empirically-based predictors of bipolar disorder. This study examined whether bipolar disorder can be predicted using clinical data and machine learning algorithms. 492 children, ages 6-18 at baseline, were recruited from longitudinal case-control family studies. Participants were assessed at baseline, then followed-up after 10 years. In addition to sociodemographic data, children were assessed with psychometric scales, structured diagnostic interviews, and cognitive and social functioning assessments. Using the Balanced Random Fore

SUBMITTER: Uchida M 

PROVIDER: S-EPMC9999264 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature