Название: Machine Learning, Big Data, and IoT for Medical InformaticsАвтор: Pardeep Kumar, Yugal Kumar, Mohamed A. Tawhid
Издательство: Academic Press, Elsevier
Год: 2021
Страниц: 433
Язык: английский
Формат: pdf (true)
Размер: 23.1 MB
Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics.
In medical informatics, Machine Learning, Big Data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of Machine Learning, Big Data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data.
Our times demand the design and development of new effective prediction systems using machine learning approaches, big data, and the Internet of Things (IoT) to meet health and life quality expectations. Furthermore, there is a need for monitoring systems that can monitor the health issues of elderly and remotely located people. In recent times, big data and IoT have played a vital role in health-related applications, mainly in disease identification and diagnosis. These techniques can provide possible solutions for healthcare analytics, in which both structured and unstructured data are collected through IoT-based devices and sensors. Machine learning and big data techniques can be applied to collected data for predictive diagnostic systems. However, designing and developing an effective diagnostic system is still challenging due to various issues like security, usability, scalability, privacy, development standards, and technologies. Therefore machine learning, big data, and IoT for medical informatics are becoming emerging research areas for the healthcare community.
This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, Machine Learning, Big Data, and IoT.
The book contains 23 chapters:
Chapter 1 presents a survey of machine learning and predictive analytics methods for medical informatics. This chapter focuses on deep neural networks with typical use cases in computational medicine, including self-supervised learning scenarios: these include convolutional neural networks for image analysis, recurrent neural networks for time series, and generative adversarial models for correction of class imbalance in differential diagnosis and anomaly detection.
Chapter 2 presents a proposed model for geolocation aware healthcare facility with IoT, Fog, and Cloud-based diagnosis in emergency cases. An end-to-end infrastructure has been modeled for the healthcare system using geolocation-enabled IoT, fog, and cloud computing technology to identify the nearest hospital or medical facility available to the patient.
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Finally, Chapter 23 focuses on machine learning in precision medicine. An overview of how machine learning is used in precision medicine and its potential use in the detection, diagnosis, prognosis, risk assessment, therapy response, and discovery of new biomarkers and drug candidates is presented in this chapter.
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