Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning
An unsupervised machine learning method and neurocognitive data used to identify two phenotypes LASSO distinguished two phenotypes using neurocognitive data with 94% accuracy. Elastic Net validates differences of the two phenotypes using FA data with 76% accuracy. Healthy controls are further used to validate differences between the two phenotypes.