A machine learning algorithm successfully screens for Parkinson's in web users.

ABSTRACT: 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

SUBMITTER: Youngmann B 

PROVIDER: S-EPMC6917308 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature