Abstract Neurodegenerative disorders, such as Parkinson’s disease (PD) and Alzheimer’s disease (AD), are important public health problems warranting early detection. We trained machine-learned classifiers on the longitudinal search logs of 31,321,773 search engine users to automatically detect neurodegenerative disorders. Several digital phenotypes with high discriminatory weights for detecting these disorders are identified. Classifier sensitivities for PD detection are 94.2/83.1/42.0/34.6% at false positive rates (FPRs) of 20/10/1/0.1%, respectively. Preliminary analysis shows similar performance for AD detection. Subject to further refinement of accuracy and reproducibility, these findings show the promise of web search digital phenotypes as adjunctive screening tools for neurodegenerative disorders.

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public health longitudinal search logs web search digital phenotypes parkinsons disease neurodegenerative disorders discriminatory weights alzheimers disease false positive rates pd detection machinelearned classifiers

Ryen W. White,P. Murali Doraiswamy,Eric Horvitz,.Detecting neurodegenerative disorders from web search signals. 1 (1),.

Ryen W. White,P. Murali Doraiswamy,Eric Horvitz,"Detecting neurodegenerative disorders from web search signals" 1

Ryen W. White,P. Murali Doraiswamy,Eric Horvitz,"Detecting neurodegenerative disorders from web search signals"