Yoon Sunmoo, Davis Nicole, Odlum Michelle, Cho Hwayoung, Broadwell Peter, Patrao Maria, Bales Michael, Alcantara Carmela, Mittelman Mary
Columbia University Irving Medical Center, USA.
School of Nursing, Clemson University, USA.
Stud Health Technol Inform. 2020 Jun 26;272:433-436. doi: 10.3233/SHTI200588.
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.
我们应用人工智能技术建立关联模型，以预测全国轻度认知障碍（MCI）护理人员样本中的总体健康状况不佳情况。我们的深度学习应用确定了年龄、护理时长、酒精摄入量、体重、心肌梗死（MI）以及黑人和西班牙裔的MCI症状频率，而对于其他人群，MCI症状频率、收入、体重、冠心病（CHD）、年龄和电子烟使用情况是进入分析的81个变量中与健康状况不佳最密切相关的因素。人工智能的应用有效地为患有MCI的黑人和西班牙裔护理人员提供了干预策略。
