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

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 for 3-year and 5-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing a major mood disorder, better able to predict the probability of developing a major mood disorder and better able to identify individuals who would be diagnosed in future time intervals. The average AUC achieved by PLANN for 5-year prediction was 0.74, which indicates good discrimination. CONCLUSIONS: This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of mood disorders in at-risk individuals and the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk offspring sample. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-021-00228-2.