Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges

被引：11
作者：
Boursalie O. [1 ]
Samavi R. [2 ,3 ]
Doyle T.E. [1 ,3 ,4 ]
机构：
[1] School of Biomedical Engineering, McMaster University, Hamilton, ON
[2] Department of Computing and Software, McMaster University, Hamilton, ON
[3] eHealth Graduate Program, McMaster University, Hamilton, ON
[4] Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON
来源：
Journal of Healthcare Informatics Research | 2018年 / 2卷 / 1-2期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Data fusion; Data mining; Health records; Machine learning; MLP; Mobile device; Remote patient monitoring; Severity estimation; SVM; System development; Wearable system;
D O I：
10.1007/s41666-018-0021-1
中图分类号：
学科分类号：
摘要：
Machine learning-based patient monitoring systems are generally deployed on remote servers for analyzing heterogeneous data. While recent advances in mobile technology provide new opportunities to deploy such systems directly on mobile devices, the development and deployment challenges are not being extensively studied by the research community. In this paper, we systematically investigate challenges associated with each stage of the development and deployment of a machine learning-based patient monitoring system on a mobile device. For each class of challenges, we provide a number of recommendations that can be used by the researchers, system designers, and developers working on mobile-based predictive and monitoring systems. The results of our investigation show that when developers are dealing with mobile platforms, they must evaluate the predictive systems based on its classification and computational performance. Accordingly, we propose a new machine learning training and deployment methodology specifically tailored for mobile platforms that incorporates metrics beyond traditional classifier performance. © 2018, Springer International Publishing AG, part of Springer Nature.
引用
收藏
页码：179 / 203
页数：24
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