智能可穿戴辅助数字医疗保健行业 5.0
Vrutti Tandel1, Aparna Kumari1, Sudeep Tanwar1
1Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat 382481, India.
Artificial intelligence in medicine
|October 31, 2024
概括
医疗保健行业5.0利用智能可穿戴设备和机器学习 (ML) 来实现个性化的患者护理. 本研究研究了用于可穿戴数据分析的ML技术,提出了一种在活动预测中实现高精度的模型.
科学领域:
- 数字健康数字健康
- 医疗保健技术 技术 医疗保健 技术
- 机器学习应用 机器学习应用
背景情况:
- 医疗保健行业5.0的发展集成了智能可穿戴设备和数字技术,增强了患者治疗和医疗保健的提供.
- 智能可穿戴设备提供了远程监控,个性化护理和远程医疗等优势,这些优势是由机器学习 (ML) 和医疗物联网 (IoMT) 等技术驱动的.
- 尽管有进展,但ML与数字医疗保健的可穿戴技术相结合的应用需要进一步探索.
研究的目的:
- 为数字医疗保健行业5.0和可穿戴技术全面检查和评估先进的ML技术.
- 为数字医疗行业5.0提出分类学和创新过程模型,解决数据收集,健康跟踪,安全和隐私方面的挑战.
- 证明ML模型在分析可穿戴数据方面的有效性,以改善患者护理和决策.
主要方法:
- 开发了数字医疗保健工业5.0的详细分类,建立了一个创新的流程模型.
- 从可穿戴设备 (如智能手表) 收集数据,然后进行数据预处理.
- 机器学习模型包括支持矢量机 (SVM),决策树 (DT) 和随机森林 (RF) 应用于活动预测和分类.
主要成果:
- 提出的基于机器学习的过程模型有效地利用智能可穿戴设备的数据进行分析.
- 机器学习算法展示了分析广泛的医疗数据的能力,包括电子健康记录 (EHR),以提供有价值的见解.
- 一个案例研究强调了随机森林 (RF) 模型的卓越性能,在活动预测中实现了0.94的最低根平均平方误差 (RMSE),0.88的平均平方误差 (MSE) 和0.27的平均绝对误差 (MAE).
结论:
- 数字医疗行业5.0,由智能可穿戴设备和ML提供动力,提供个性化,主动和以患者为中心的医疗保健解决方案.
- 该研究提供了一个强大的框架,并验证了ML技术在医疗保健中分析可穿戴数据的使用.
- 这些发现强调了ML,特别是RF在提高活动预测准确性和改善医疗保健决策过程方面的潜力.
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