机器学习方法来发现健康衰老的福祉和弹性隐藏的模式
Robin R Austin1, Ratchada Jantraporn1, Martin Michalowski1
1School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA.
概括
机器学习分析了使用MyStrengths+MyHealth应用程序从45岁以上的成年人身上获得的全人健康数据. 调查结果揭示了显著的优势和需求,特别是在"思考"领域,为健康老龄化策略提供信息.
科学领域:
- 老年学是一门学科.
- 数字健康数字健康
- 医疗保健中的机器学习
背景情况:
- 对健康老龄化的整体方法至关重要,考虑到社会决定因素.
- 移动健康 (mHealth) 应用程序提供了在临床访问之间收集数据的新方法.
- 机器学习 (ML) 和人工智能 (AI) 可以揭示对健康衰老的见解,护士发挥着关键作用.
研究的目的:
- 将ML方法应用于45岁及以上成年人未被识别的MyStrengths+MyHealth数据.
- 用ML探索与健康衰老相关的模式和见解.
主要方法:
- 对988名成年人 (45岁以上) 的非识别数据的回顾性分析.
- 使用了以ML方法为指导的探索性数据分析过程.
- 数据包括强项,挑战和需求的指标.
主要成果:
- 平均分数:优势66.1%,挑战66.5%,需要60.06%.
- 在优势和需求 (p<0.001) 和挑战和需求 (p<0.001) 之间发现了显著的差异.
- 确定了四个概念组:思考,运动,情绪,睡眠. 思考小组显示了最高的优势,挑战和需求.
结论:
- 应用到消费者健康数据上的ML为健康老龄化提供了独特的见解.
- 45岁以上的成年人表现出了具有显著优势的弹性,尽管面临挑战和需求.
- 思考领域的高参与度突出了共同出现的健康问题,并为未来的个性化干预提供了信息,由结合AI的护士领导.
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