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In Studies in health technology and informatics ; h5-index 23.0

We applied artificial intelligence techniques to build correlate models that predict general poor health in a national sample of caregivers with mild cognitive impairment (MCI). Our application of deep learning identified age, duration of caregiving, amount of alcohol intake, weight, myocardial infarction (MI) and frequency of MCI symptoms for Blacks and Hispanics whereas frequency of MCI symptoms, income, weight, coronary heart disease (CHD), age, and use of e-cigarette for the others as the strongest correlates of poor health among 81 variables entered. The application of artificial intelligence efficiently provided intervention strategies for Black and Hispanic caregivers with MCI.

Yoon Sunmoo, Davis Nicole, Odlum Michelle, Cho Hwayoung, Broadwell Peter, Patrao Maria, Bales Michael, Alcantara Carmela, Mittelman Mary


artificial intelligence, health disparities, mild cognitive impairment