Predicting The Risk and Timing of Bipolar Disorder In Offspring of Bipolar Parents: Exploring The Utility of a Neural Network Approach

Abstract BackgroundBipolar 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 bipolar spectrum disorders Results Overall, for predictive performance, PLANN outperformed the more traditional logistic model for one year, three year and five-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing bipolar disorder, better able to predict the probability of developing bipolar disorder and had higher accuracy than the logistic model. ConclusionsThis evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of diagnosis of mental health for at-risk individuals and demonstrated the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk sample.