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Integrating eSAGE With EHR Data Using Machine Learning for the Early Detection and Monitoring of Cognitive Impairment in Individuals
Integrating eSAGE With EHR Data Using Machine Learning for the Early Detection and Monitoring of Cognitive Impairment in Individuals

NCT06017505

Enrolling By InvitationN/A

Sponsor: Douglas Scharre

Conditions: Dementia, Alzheimer Disease, Mild Cognitive Impairment, Worried Well

Interventions: electronic self administered gerocognitive examination (eSAGE)

Countries: United States

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.

Eligibility overview

Sex: ALL

Age: 50 Years to —

Healthy volunteers: No

Study type: OBSERVATIONAL

Eligibility criteria
Inclusion Criteria:

* 1\. Males and females 50 years of age and over who complete the eSAGE as part of their office visit at the Center for Cognitive and Memory Disorders.

Exclusion Criteria:

* None
Locations (1)
  • Columbus, Ohio, United States