Predicting the onset of Alzheimer’s disease and related dementia using electronic health records: findings from the cache county study on memory in aging (1995–2008)
Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict "gold-standard" research-based AD/ADRD diagnoses, corroborated by prior research. Using ICD diagnostic codes to identify dementia as done in the majority of machine learning dementia prediction models, as compared to "gold-standard" dementia diagnoses, can result in higher accuracy, but whether these models are predicting true dementia warrants further research.
Authors
- Julio C. Facelli (ORCID: https://orcid.org/0000-0003-1449-477X)
- Truls Østbye (ORCID: https://orcid.org/0000-0002-0662-7440)
- Samir Abdelrahman (ORCID: https://orcid.org/0000-0001-6396-3259)
- JoAnn T. Tschanz (ORCID: https://orcid.org/0000-0003-1766-2556)
- Huong Meeks (ORCID: https://orcid.org/0000-0001-8712-4679)
- Karen C. Schliep (ORCID: https://orcid.org/0000-0003-1771-3157)
- Ken R. Smith (ORCID: https://orcid.org/0000-0003-0682-3705)
- Michael W. Varner (ORCID: https://orcid.org/0000-0001-9455-3973)
- Michelle Sorweid
- Richard D. Boyce (ORCID: https://orcid.org/0000-0002-2993-2085)
- Christine J. Cliatt Brown
- Jeffrey Thornhill
Institutions
- Utah State University (US)
- University of Pittsburgh (US)
- University of Utah (US)
- Institute for Community Health (US)
- Utah Department of Health (US)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1186/s12911-024-02728-4
- Citations
- 3
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 1.16
Funders
- Utah Department of Health
- Utah State University
- Huntsman Cancer Foundation
- Huntsman Cancer Institute
- Center on Aging, University of Utah
- National Institute on Aging
- National Cancer Institute
- National Center for Research Resources