مقاله انگلیسی رایگان در مورد یادگیری ماشین در سلامت هوشمند – الزویر 2019

1. Introduction

2. Smart Health

3. ML Approaches in Smart Health

4. Conclusions and Future Work

References

The increase of age average led to an increase in the demand of providing and improving the service of healthcare. The advancing of the information and communication technology (ICT) led to the development of smart cities which have a lot of components. One of those components is Smart Health (s-Health), which is used in improving healthcare by providing many services such as patient monitoring, early diagnosis of diseases and so on. Nowadays there are many machine learning techniques that can facilitates s-Health services. This paper reviews recent published papers in the area of smart health starting from the years 2011 to 2017, and a structured analysis for different machine learning (ML) approaches that are applied in s-Health. The results show that the ML approach is used in many s-Health applications such as Glaucoma diagnosis, Alzheimer’s disease, bacterial sepsis diagnoses, the Intensive Care Unit (ICU) readmissions, and cataract detection. The Artificial Neural Network (ANN), Support Vector Machine (SVM) algorithm and deep learning models especially the Convolutional Neural Network (CNN) are the most commonly used machine learning approaches where they proved to get high evaluation performance in most cases.

Introduction

e-Health can be defined as “an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology.”3. There is an intersection between s-Health and Mobile Health (m-Health); m-Health can be defined as “emerging mobile communications and network technologies for healthcare systems”4. Machine Learning (ML) is a field that grew out of Artificial Intelligence (AI). It is concerned with designing and developing algorithms that enable the computers to evolve their behaviors according to empirical data. The ML approach is evolving rapidly as a result of the improvement of the ML algorithms, enhanced methods of capturing data, improved computer networks, new sensors/IO units, and the interest in self-customization to users’ behavior 5.