论文标题
协作机器学习驱动的医学事物互联网 - 系统文献评论
Collaborative Machine Learning-Driven Internet of Medical Things -- A Systematic Literature Review
论文作者
论文摘要
对医疗保健的物联网设备的采用越来越多，使研究人员能够使用这些设备产生的所有数据来构建情报。监测和诊断健康一直是这种设备有益的两个最常见的情况。最初，实现高预测准确性是首要任务，但是事实证明，重点已逐渐转移到效率和更高的吞吐量上，并以分布式方式处理这些设备的数据已证明可以帮助实现这两者。由于机器学习领域具有许多最新的算法，因此确定在不同情况下表现最佳的算法是一个挑战。在本文献综述中，我们探索了由选定研究的作者测试的分布式机器学习算法，并确定了在每个医疗保健方案中达到最佳预测准确性的分布式机器学习算法。虽然没有算法始终执行，但随机森林在一些研究中表现最好。这可能是对IOMT数据协作机器学习的未来研究的好起点。
The growing adoption of IoT devices for healthcare has enabled researchers to build intelligence using all the data produced by these devices. Monitoring and diagnosing health have been the two most common scenarios where such devices have proven beneficial. Achieving high prediction accuracy was a top priority initially, but the focus has slowly shifted to efficiency and higher throughput, and processing the data from these devices in a distributed manner has proven to help achieve both. Since the field of machine learning is vast with numerous state-of-the-art algorithms in play, it has been a challenge to identify the algorithms that perform best in different scenarios. In this literature review, we explored the distributed machine learning algorithms tested by the authors of the selected studies and identified the ones that achieved the best prediction accuracy in each healthcare scenario. While no algorithm performed consistently, Random Forest performed the best in a few studies. This could serve as a good starting point for future studies on collaborative machine learning on IoMT data.
