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Parkinson's disease (PD) is a slowly progressing neurodegenerative diseasewith early manifestation of motor signs. Recently, there has been a growinginterest in developing automatic tools that can assess motor function in PDpatients. Here we show that mouse tracking data collected during people'sinteraction with a search engine can be used to distinguish PD patients fromsimilar, non-diseased users and present a methodology developed for thediagnosis of PD from these data. A main challenge we address is the extractionof informative features from raw mouse tracking data. We do so in twocomplementary ways: First, we manually construct expert-recommended informativefeatures, aiming to identify abnormalities in motor behaviors. Second, we usean unsupervised representation learning technique to map these raw data tohigh-level features. Using all the extracted features, a Random Forestclassifier is then used to distinguish PD patients from controls, achieving anAUC of 0.92, while results using only expert-generated or auto-generatedfeatures are 0.87 and 0.83, respectively. Our results indicate that mousetracking data can help in detecting users at early stages of the disease, andthat both expert-generated features and unsupervised techniques for featuregeneration are required to achieve the best possible performance

Detecting Parkinson's Disease from interactions with a search engine: Is expert knowledge sufficient?