一种基于人工智能的新型合作性摔倒风险预测模型,适用于老年人.
Deepika Mohan1, Peter Han Joo Chong1, Jairo Gutierrez2
1Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究介绍了一种合作型人工智能 (AI) 模型,用于使用生命体征和日常活动预测老年人倒. 人工智能系统实现了高精度,提高了老年人的安全性和预防性护理.
科学领域:
- 老年学是指老年学的学科.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 老年人是医疗保健服务的重要用户,容易跌倒.
- 需要具有成本效益的电子健康技术来支持老年人的独立生活.
- 人工智能 (AI) 和机器学习 (ML) 显示出对跌倒预测和健康监测的潜力.
研究的目的:
- 引入一种新的合作性人工智能模型,用于预测老年人中跌倒风险.
- 为了提高准确性,将两个AI模型的预测结合起来.
- 改善老年人的预防护理和福祉.
主要方法:
- 开发了一种集成两个不同的AI预测器的合作AI模型.
- AI1模型使用基于生命体征的模糊逻辑.
- AI2模型使用深度信念网络 (DBN) 分析日常生活活动 (ADL).
- 一个元模型将输出组合起来,用于全面预测落风险.
主要成果:
- 合作性AI模型显示了85.71%的灵敏度.
- 在降落风险预测方面实现了100%的特异性.
- 与莫尔斯布尺度 (MFS) 相比,报告了90.00%的整体预测准确度.
结论:
- 基于深度学习的合作系统可以显著改善独自生活的老年人的福祉.
- 拟议的模型提供了一种更精确的方法来评估跌倒风险.
- 改进的跌倒风险评估有助于改善老年人的预防性护理策略.
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