Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach.

ABSTRACT:

Background

Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of major mood disorders in 1, 3 and 5-year intervals.

Results

Overall, for predictive performance, PLANN outperformed the more traditional discrete survival model

SUBMITTER: Cooper A 

PROVIDER: S-EPMC8245610 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature