Investigating the Best Performing Task Conditions of a Multi-Tasking Learning Model in Healthcare Using Convolutional Neural Networks: Evidence from a Parkinson'S Disease Database

This paper presents three conditions of Multi-Task Learning (MTL) model architectures based on Deep Neural Networks (DNNs) to predict the Parkinson's Disease (PD) from brain images. It also demonstrates the usefulness of incorporating additional patients' contextual epidemiological information (i.e. their age and sex). Our aim is to investigate which are the patients' clinical data, that when joined with the primary task could perform best and thus provide an improved computational PD prediction model. Our proposed model architectures are evaluated on a new medical dataset, which is presently under development. Our preliminary results suggest the robustness of our proposed systems to analyze and provide an accurate estimate of the status of the disease. Finally, we discuss the lessons learned from our experimental settings with respect to addressing several research questions such as the importance of selecting the auxiliary task(s) respectively, which is the best performing task combination to achieve such improved Parkinson's disease prediction, as well as whether all auxiliary tasks are equally effective.