Detecting neurodegenerative disorders from web search signals

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 neurodegenerat...

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Bibliographic Details
Published in:	NPJ digital medicine Vol. 1; no. 1; p. 8
Main Authors:	White, Ryen W., Doraiswamy, P. Murali, Horvitz, Eric
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 23-04-2018 Nature Publishing Group
Subjects:	692/617/375 692/700/478 Alzheimer's disease Biomedicine Biotechnology Brief Communication Digital technology Health informatics Medicine Medicine & Public Health Parkinson's disease Neurological disorders Public health
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2398-6352 2398-6352
DOI:	10.1038/s41746-018-0016-6