Applying Artificial Intelligence to Predict Self-Reported Poor Health Among Black and Hispanic Caregivers with Mild Cognitive Impairment

机译：应用人工智能预测黑人和西班牙裔护理人员的自我报告的健康，具有轻度认知障碍

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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.

机译：我们应用人工智能技术来构建相关模型，以预测具有轻度认知障碍（MCI）的国家护理人员样本中的一般健康状况。我们应用深层学习的应用程序确定年龄，持续时间，酒精摄入量，体重，心肌梗死（MI）和MCI症状的频率为黑人和西班牙裔，而MCI症状，收入，体重，冠心病（CHD）的频率，年龄，以及其他人的电子香烟作为进入81个变量之间的健康状况的最强相关性。人工智能的应用有效地为MCI提供了黑白护理护理人员的干预策略。

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《International Conference on Informatics, Management, and Technology in Healthcare》|2020年|xix 499 p. :|共4页
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Sunmoo Yoon; Nicole Davis; Michelle Odlum; Hwayoung Cho; Peter Broadwell; Maria Patrao; Michael Bales; Carmela Alcantara; Mary Mittelman;
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4. Applying Artificial Intelligence to Predict Self-Reported Poor Health Among Black and Hispanic Caregivers with Mild Cognitive Impairment [C] . Sunmoo Yoon, Nicole Davis, Michelle Odlum, International Conference on Informatics, Management, and Technology in Healthcare . 2020

机译：应用人工智能预测黑人和西班牙裔护理人员的自我报告的健康，具有轻度认知障碍
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Applying Artificial Intelligence to Predict Self-Reported Poor Health Among Black and Hispanic Caregivers with Mild Cognitive Impairment

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