Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services
Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services
复制标题
DOI：
10.1007/978-3-030-28553-1_6
复制
发表时间：
2020-01-01
期刊：
影响因子：
--
通讯作者：
Aljaaf, Ahmed J.
中科院分区：
文献类型：
--
作者：
Alloghani, Mohamed;Baker, Thar;Aljaaf, Ahmed J.
电子健康的出现以及对实时患者监测和评估的需求促使人们对了解人们的行为以改善护理服务产生了兴趣。本文研究了机器学习算法在生命体征聚类和预测中的应用。在大数据和围绕生命体征的争论的背景下，数据在预测医学中正迅速变得更加相关和适用。本文评估了k-Means和x-Means在聚类信号中的适用性，并使用深度学习、朴素贝叶斯、随机森林、决策树和广义线性模型来预测基于人体动态运动的生命信号模式。
The advent of eHealth and the need for real-time patient monitoring and assessment has prompted interest in understanding people behavior for improving care services. In this paper, the application ofmachine learning algorithms in clustering and predicting vital signs was pursued. In the context of big data and the debate surrounding vital signs data is fast becoming more relevant and applicable in predictive medicine. This paper assesses the applicability of k-Means and x-Means in clustering signals and used deep learning, Naive Bayes, Random Forests, Decision Trees, and Generalized Linear Models to predict human dynamic motion-based vital signal patterns.