A machine learning approach to multifactorial modeling of episodic memory performance
Decision trees may be a promising approach to determining the relative contributions of multiple life exposure factors that affect cognitive performance in a large and diverse cohort. This may help to inform ADRD preventive interventions.
Authors
- Yi Lor (ORCID: https://orcid.org/0000-0002-6598-9468)
- Zvinka Z. Zlatar (ORCID: https://orcid.org/0000-0002-8690-6406)
- Kristen M. George (ORCID: https://orcid.org/0000-0001-8773-7678)
- Umesh M. Venkatesan (ORCID: https://orcid.org/0000-0003-1869-8083)
- Ruijia Chen (ORCID: https://orcid.org/0000-0002-2759-9290)
- Marianne Chanti‐Ketterl (ORCID: https://orcid.org/0000-0002-0713-1439)
- Emily Hokett (ORCID: https://orcid.org/0000-0001-7171-8237)
- Rachel A. Whitmer (ORCID: https://orcid.org/0009-0006-9511-6138)
- Evan Fletcher
- Paola Gilsanz (ORCID: https://orcid.org/0000-0002-7635-381X)
Institutions
- Kaiser Permanente (US)
- Thomas Jefferson University (US)
- Duke University (US)
- University of California, San Francisco (US)
- Columbia University Irving Medical Center (US)
- University of California San Diego (US)
- Moss Rehabilitation Hospital (US)
- University of California, Davis (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/alz.089749
- Primary Topic
- Health, Environment, Cognitive Aging
- Type
- article
- Field-Weighted Citation Impact
- 0.00