Project Summary As the 6th leading cause of death in the US, Alzheimer's disease (AD) and Alzheimer's disease-related dementias (ADRD) affect about 5.7 million Americans. However, up until now, our understanding of risk factors of AD/ADRD is still limited and our efforts on developing effective treatments for AD/ADRD have been greatly disappointing. Therefore, there is an urgent need to develop new methods to conduct AD/ADRD research more efficiently. One of the potential approaches is to leverage large, longitudinal, observational clinical data accumulated in electronic health records (EHRs). Nevertheless, current uses of EHRs for AD/ADRD research is very limited, often requiring manual data extraction and normalization (i.e., manual chart review), which is labor-intensive and time-consuming. Therefore, in this study, we plan to develop novel ontology and natural language processing (NLP) based informatics methods and tools to automatically extract and normalize AD/ADRD-related clinical data in EHRs, thus facilitating efficient AD/ADRD observational studies using EHRs. We propose the following three specific aims to achieve this goal: 1) Build an information model for EHR-based AD/ADRD research using a formal ontology representation approach; and 2) Extract and normalize AD/ADRD information in clinical documents using NLP technologies; and 3) Evaluate developed informatics methods and tools through demonstration studies and disseminate them to support observational AD/ADRD research.
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