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Using ensemble learning techniques and longitudinal data from a large naturalistic driving study, researchers at Columbia University's Mailman School of Public Health, Fu Foundation School of Engineering and Applied Science, and Vagelos College of Physicians and Surgeons have developed a novel, interpretable and highly accurate algorithm for predicting mild cognitive impairment and dementia in older drivers.
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April 28, 2021 Using naturalistic driving data and machine learning techniques, researchers at Columbia University Mailman School of Public Health and Columbia s Fu Foundation School of Engineering and Applied Science have developed highly accurate algorithms for detecting mild cognitive impairment and dementia in older drivers. Naturalistic driving data refer to data captured through in-vehicle recording devices or other technologies in the real-world setting. These data could be processed to measure driving exposure, space and performance in great detail. The findings are published in the journal
Geriatrics.
The researchers developed random forests models, a statistical technique widely used in AI for classifying disease status, that performed exceptionally well. Based on variables derived from the naturalistic driving data and basic demographic characteristics, such as age, sex, race/ethnicity and education level, we could predict mild cognitive impairment an