  Parkinson's disease (PD) is a slowly progressing neurodegenerative disease
with early manifestation of motor signs. Recently, there has been a growing
interest in developing automatic tools that can assess motor function in PD
patients. Here we show that mouse tracking data collected during people's
interaction with a search engine can be used to distinguish PD patients from
similar, non-diseased users and present a methodology developed for the
diagnosis of PD from these data. A main challenge we address is the extraction
of informative features from raw mouse tracking data. We do so in two
complementary ways: First, we manually construct expert-recommended informative
features, aiming to identify abnormalities in motor behaviors. Second, we use
an unsupervised representation learning technique to map these raw data to
high-level features. Using all the extracted features, a Random Forest
classifier is then used to distinguish PD patients from controls, achieving an
AUC of 0.92, while results using only expert-generated or auto-generated
features are 0.87 and 0.83, respectively. Our results indicate that mouse
tracking data can help in detecting users at early stages of the disease, and
that both expert-generated features and unsupervised techniques for feature
generation are required to achieve the best possible performance
