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Updated: Jan 22, 2026
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Quantitative Analysis of Climbing Defects in a Drosophila Model of Neurodegenerative Disorders
Published on: June 13, 2015
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Detecting neurodegenerative disorders from web search signals
Ryen W White1, P Murali Doraiswamy2, Eric Horvitz3
11Microsoft, Bellevue, WA 98004 USA.
NPJ Digital Medicine
|July 16, 2019
Summary
Machine learning models analyzing search engine data can help detect neurodegenerative diseases like Parkinson's disease (PD) and Alzheimer's disease (AD) early. This approach shows promise for developing new screening tools for these conditions.
Area of Science:
- Computational neuroscience
- Digital epidemiology
- Neurology
Background:
- Neurodegenerative disorders, including Parkinson's disease (PD) and Alzheimer's disease (AD), pose significant public health challenges.
- Early detection of these conditions is crucial for timely intervention and management.
Purpose of the Study:
- To develop and evaluate machine-learned classifiers for the automatic detection of neurodegenerative disorders using search engine data.
- To identify digital phenotypes from user search logs that are indicative of PD and AD.
Main Methods:
- Trained machine-learned classifiers on longitudinal search logs from over 31 million users.
- Analyzed search data to identify digital phenotypes with high discriminatory power for neurodegenerative diseases.
Main Results:
- Achieved high sensitivities for Parkinson's disease detection, reaching 94.2% at a 20% false positive rate.
- Preliminary results indicate similar promising performance for Alzheimer's disease detection.
- Identified several digital phenotypes with significant discriminatory weights for disease detection.
Conclusions:
- Web search digital phenotypes show potential as adjunctive screening tools for neurodegenerative disorders.
- Further research is needed to refine accuracy and ensure reproducibility of these findings.
- This study highlights the utility of large-scale digital data in early disease detection.
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