Using Machine Learning to Identify Dementia Risk
Author Information
Author(s): Ayushi Divecha, Sarah Bannon, Kristen Dams-O’Connor
Primary Institution: Icahn School of Medicine at Mount Sinai
Hypothesis
Can machine learning models effectively identify individuals at risk of developing dementia?
Conclusion
The study aims to use machine learning to predict dementia risk in cognitively normal individuals.
Supporting Evidence
- The study uses data from five large NIH-funded studies of aging.
- 2235 individuals were included in the analysis with a mean follow-up of 8.6 years.
- Random forest models will be used to predict dementia risk.
Takeaway
Researchers are using computers to help find people who might get dementia in the future, so they can get help early.
Methodology
The study uses clinical and cognitive data from large NIH-funded studies and applies random forest models to predict dementia risk.
Limitations
Cognitive function was assessed using different measures across cohorts, which may affect comparisons.
Participant Demographics
Participants were cognitively normal individuals from three NIH-funded cohorts.
Digital Object Identifier (DOI)
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