Enable access to electronic health records (EHR) data and provide support for their integration with clinical and molecular data to build person-specific predictive models of disease and wellness and to enable disease sub-classification. These efforts should include better electronic phenotyping of AD through the application of machine learning methods.
Hold an advisory meeting focused on understanding and overcoming barriers to sharing and usability of electronic health records in the US and globally.
Initiate a research program focused on electronic phenotyping of AD through the application of machine learning methods and integration of EHR data with genomic, molecular, clinical and other patient relevant data to build person-specific models of disease, to identify disease sub-types and to identify biomarkers and molecular signatures for these subtypes.
Summary of Key Accomplishments
In 2021, NIA supported an expert panel meeting that identified opportunities for: expanding data access — including electronic health records — and integration to support interventional AD/ADRD studies; using big data to address health disparities across AD/ADRD populations; public-private data sharing collaborations and data licensing; and strategies for using big data for clinical interventions.
The use of EHRs is an integral part of many NIA-supported population studies, including the Psych-AD program and the Drug Repurposing and Combination Therapy program. These projects seek to develop interactive web applications and tools for the broad scientific community that can be used to explore the links between neuropsychiatric symptoms and AD.
This information is current as of March 2022.
- Research Implementation Area
- Population Studies and Precision Medicine
- In Progress
- PAR-18-596: Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R01)
- NOT-AG-18-001 (#9): Neuropsychiatric Symptoms in Alzheimer's Disease (NPS-AD)
Research Programs and Resources
- Construct Large-Scale Phenomes of Diseases and Drugs and Develop Data-Driven Systems Approaches to Understand Genetic Links Between Alzheimer’s Disease and Neuropsychiatric Symptoms