Publication: Predicting Cognitive Impairment and Dementia: A Machine Learning Approach
Predicting Cognitive Impairment and Dementia: A Machine Learning Approach
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Date
Date
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Aschwanden, D., Aichele, S., Ghisletta, P., Terracciano, A., Kliegel, M., Sutin, A. R., Brown, J., & Allemand, M. (2020). Predicting Cognitive Impairment and Dementia: A Machine Learning Approach. Journal of Alzheimer’s Disease, 75(3), 717–728. https://doi.org/10.3233/JAD-190967
Abstract
Abstract
Abstract
BACKGROUND: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking.
OBJECTIVE: We used a combined methodology of machine learning and semi-parametric survival analysis to estimate the relative importance of 52 predictors in forecasting cognitive impairment and dementia in a large, population-representative sample of older adults.
METHODS: Participants
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Creators (Authors)
- Aschwanden, Damaris
- Aichele, Stephen
- Ghisletta, Paolo
- Terracciano, Antonio
- Kliegel, Matthias
- Sutin, Angelina R
- Brown, Justin
- Allemand, Mathias
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Free Access at
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National Institute on Aging of the National Institutes of Health
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R21AG057917 and R01AG05329
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Citations
Aschwanden, D., Aichele, S., Ghisletta, P., Terracciano, A., Kliegel, M., Sutin, A. R., Brown, J., & Allemand, M. (2020). Predicting Cognitive Impairment and Dementia: A Machine Learning Approach. Journal of Alzheimer’s Disease, 75(3), 717–728. https://doi.org/10.3233/JAD-190967
