000			05955cam a22005298i 4500
001			on1243014698
003			OCoLC
005			20220526093038.0
006			m d
007			cr |||||||||||
008			210317s2021 nyu ob 001 0 eng
010			_a 2021012335
020			_a1536194689
020			_a9781536194685 _q(electronic bk.)
020			_z9781536194203 _q(hardcover)
035			_a2892990
035			_a(OCoLC)1243014698
040			_aDLC _beng _erda _cDLC _dOCLCO _dYDX _dOCLCF _dN$T
042			_apcc
049			_aMAIN
050	0	0	_aR119.9
082	0	0	_a610.285 _223
245	0	0	_aMobile health : _badvances in research and applications / _cGaurav Gupta, Assistant Professor, Yogananda School of AI Computers and Data Science, Shoolini University, Solan, H.P. India [and three others]. Nagesh Kumar, Yashwant Singh, Varun Jaiswal.
263			_a2106
264		1	_aNew York : _bNova Science Publishers, _c[2021]
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aHealth care in transition
504			_aIncludes bibliographical references and index.
520			_a"Smart health technologies continue to gain research interest across the globe in this digital era. Researchers are focusing on advancements in healthcare systems to make human life better. Also, such advancements help in early disease diagnosis and prevention of the worst diseases. Designing smart healthcare systems is possible only because of recent developments in artificial intelligence, machine learning and IoT technologies. Though mHealth refers to all mobile devices which can communicate data, mobile phones are presently the most popular platform for mHealth delivery. Ninety-four percent of the world population owns/uses a mobile phone, making mobile phones an optimal delivery platform for mHealth interventions. mHealth may catalyse the healthcare delivery model from a historical/episodic model into a tangible/patient-centric model. mHealth is being viewed progressively by many as an essential technology metaphor to achieve rich, vigorous patient engagement, ultimately achieving a patient-centric paradigm change. This book will discuss diverse topics to explain the rapidly emerging and evolving mobile health and artificial perspective, the emergence of integrated platforms and hosted third-party tools, and the development of decentralized applications for various research domains. It presents various applications that are helpful for research scholars and scientists who are working toward identifying and pinpointing the potential of as well as the hindrances to mHealth. The wide variety in topics it presents offers readers multiple perspectives on a variety of disciplines. The aim of this edited book is to publish the latest research advancements in the convergence of automation technology, artificial intelligence, biomedical engineering and health informatics. This will help readers to grasp the extensive point of view and the essence of recent advances in this field. This book solicits contributions which include theory, case studies and computing paradigms pertaining to healthcare applications. The prospective audience would be researchers, professionals, practitioners, and students from academia and industry who work in this field. We hope the chapters presented will inspire future research from both theoretical and practical viewpoints to spur further advances in the field. A brief introduction about each chapter follows. Chapter 1 focuses on the role of Internet of Things (IoT) technologies in healthcare which provides an overview of the various types of IoT devices and data generating equipment for medical information. In Chapter 2, the objective is to provide a brief discussion about the advantages and disadvantages of using IoT based technologies in healthcare such as wearable devices. Chapter 3 deals with important aspects of data science for healthcare systems, which includes various algorithms for decision support system algorithms. Chapter 4 discusses various innovative technologies like digital twins for healthcare and medical diagnosis. Chapter 5 discusses research investigating the long-term effects of pregnancy and lactation on the female body. Chapter 6 summarizes recent advances in machine and deep learning techniques for smart healthcare applications. Chapter 7 explores the research insights on using an artificial neural network with a wrapper-based feature selection to predict heart failure. Chapter 8 presents a review on context-aware mobile healthcare for smart health services in nursing homes. Chapter 9 focuses on certain machine learning methods that can help in early prediction of pandemics. Chapter 10 explores techniques and methods based on machine learning for malaria diagnosis. Chapter 11 is a complete discussion about mobile health technology to improve health-related quality of life of chronic disease patients in emerging economies"-- _cProvided by publisher.
588			_aDescription based on print version record and CIP data provided by publisher; resource not viewed.
590			_aAdded to collection customer.56279.3
650		0	_aTelecommunication in medicine.
650		0	_aMobile communication systems.
650		0	_aMedical technology.
650		7	_aMedical technology. _2fast _0(OCoLC)fst01014742
650		7	_aMobile communication systems. _2fast _0(OCoLC)fst01024207
650		7	_aTelecommunication in medicine. _2fast _0(OCoLC)fst01146012
655		4	_aElectronic books.
700	1		_aGupta, Gaurav, _eeditor.
776	0	8	_iPrint version: _tMobile health _dNew York : Nova Science Publishers, [2021] _z9781536194203 _w(DLC) 2021012334
850			_aSHTL
856	4	0	_3EBSCOhost _uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2892990
942			_2nlm _cEBK
999			_c36094 _d36094