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Integrating eSAGE With EHR Data Using Machine Learning for the Early Detection and Monitoring of Cognitive Impairment in Individuals
Sponsor: Douglas Scharre
Summary
The goal of this observational trial is to leverage the electronic Self-Administered Gerocognitive Examination (eSAGE), a variety of metadata (a set of data that describes and gives information about other data) collected during eSAGE testing, electronic health records (EHR) information, and advanced machine learning (ML) techniques to develop a new tool that can aid in early-stage prediction of individuals with cognitive impairments.
Official title: Integrating the Electronic Self-administered Gerocognitive Examination (eSAGE) With Electronic Health Records (EHR) Data Using Machine Learning (ML) for the Early Detection and Monitoring of Cognitive Impairment in Individuals
Key Details
Gender
All
Age Range
50 Years - Any
Study Type
OBSERVATIONAL
Enrollment
1486
Start Date
2024-09-01
Completion Date
2027-09
Last Updated
2025-03-21
Healthy Volunteers
No
Interventions
electronic self administered gerocognitive examination (eSAGE)
A self-administered digital assessment that evaluates multiple cognitive domains: orientation, language, memory, executive function, calculations, abstraction, and visuospatial abilities, through multiple questions. Additionally, it includes the collection of six clinical variables: education, gender, race, family history of dementia, stroke, and emotion.
Locations (1)
Nicole Vrettos
Columbus, Ohio, United States