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

IF 5.9 Q1 Computer Science
Journal of Healthcare Informatics Research Pub Date : 2018-05-22 eCollection Date: 2018-06-01 DOI:10.1007/s41666-018-0021-1
Omar Boursalie, Reza Samavi, Thomas E Doyle
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引用次数: 0

Abstract

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.

机器学习与移动健康监测平台:关于研究与实施挑战的案例研究。
基于机器学习的患者监测系统通常部署在远程服务器上,用于分析异构数据。虽然最近移动技术的进步为直接在移动设备上部署此类系统提供了新的机会,但研究界并未广泛研究开发和部署所面临的挑战。在本文中,我们系统地研究了在移动设备上开发和部署基于机器学习的病人监护系统的各个阶段所面临的挑战。针对每一类挑战,我们都提出了一些建议,可供研究人员、系统设计人员和开发人员在开发基于移动设备的预测和监控系统时参考。我们的调查结果表明,当开发人员处理移动平台时,他们必须根据预测系统的分类和计算性能对其进行评估。因此,我们提出了一种专为移动平台量身定制的新机器学习训练和部署方法,其中包含了传统分类器性能以外的指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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