Multi-Task Learning for Predicting Parkinson's Disease Based on Medical Imaging Information

Parkinson's disease (PD) is a long-term degenerative disorder of the central nervous system, with symptoms generally appearing slowly over time. Predicting the PD disease is critical as motor and non-motor manifestations occur many years after the onset of neurodegeneration, hence its early management of disease is a significant challenge in the field of PD therapeutics. While part of previous studies with respect to the prediction of Parkinson's Disease has been based mainly on brain images, dependencies between additional patients' information have not been taken into account. This observation suggests that prediction of Parkinson's Disease along with additional patients' data with a unified framework should outperform Machine Learning (ML) algorithms that treat different sources of patients' information separately. Our presented framework relies on Multi-Task Learning (MTL) implemented with Deep Neural Networks (DNNs) with shared hidden layers. Our preliminary experimental results confirm the benefits of MTL over Single-Task Learning (STL), underlying the capability of our proposed system to achieve an increased Area Under the Curve (AUC) as high as 92% and helping at the same time to reduce human error.