A machine-learning algorithm successfully screens for Parkinson’s in web users

Objective: To develop, apply, and evaluate, a novel web-based classifier for screening for Parkinson disease among a large cohort of search engine users. Methods: A supervised machine learning classifier learned to distinguish web users with self-reported Parkinson’s disease from controls based on their interactions with a search engine (Bing, Microsoft). It was then applied to groups of web users with low or high risk for actual Parkinson’s disease. Textual content of web queries was used to sort surfers into the different risk groups, but not for classifying users as negative or positive for Parkinson’s disease. Disease detection was unsolicited. Researchers did not have access to any identifying data on users. Results: Applying the classifier (with an estimated positive predictive value of 25%) resulted in 17,843/1,490,987 (1.2%) web users over the age of 40 years screened positive for Parkinson’s disease. This percentile was higher in at-risk groups (Fisher exact P < 0.00001), including users who searched for information regarding the disease (518/804, 64.4%), and users with non-motor Parkinson’s symptom or with an affected relative (57/1064, 5.3%). Longitudinal follow-up revealed that in all studied groups individuals classified as having the disease showed a higher mean rate of progression in disease-related features (t-test P < 0.05). Interpretation: An automatic classifier, based on mouse and keyboard interactions with a search engine, is able to reliably trace individuals at high risk for actual Parkinson’s disease as well as to demonstrate more rapid progression of disease-related signs in those who screened positive. This ability raises novel ethical issues.