In a new study from Columbia University, researchers 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.
In the study, the team 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, they could predict mild cognitive impairment and dementia with 88% accuracy.
The team constructed 29 variables using the naturalistic driving data captured by in-vehicle recording devices from 2977 participants.
At the time of enrollment, the participants were active drivers aged 65-79 years and had no strong cognitive impairment and degenerative medical conditions.
Among the 2977 participants whose cars were instrumented with in-vehicle recording devices, 33 were newly diagnosed with mild cognitive impairment and 31 with dementia by April 2019.
The researchers trained a series of machine learning models for detecting mild cognitive impairment/dementia and found that the model based on driving variables and demographic characteristics was 88% accurate.
Further analysis revealed that age was most predictive of mild cognitive impairment and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, length of trips starting and ending at home, minutes per trip, and so on.
The team says driving is a complex task involving dynamic cognitive processes and requiring essential cognitive functions and perceptual-motor skills.
This study indicates that naturalistic driving behaviors can be used as comprehensive and reliable markers for mild cognitive impairment and dementia.
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The study is published in Geriatrics. One author of the study is Sharon Di.
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