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020			_a9781119792611 _qelectronic
024	7		_a10.1002/9781119792611 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041	0		_aeng
060			_aW 26.5 _bM149 2021
090			_aW 26.5 _bM149 2021EBK
245	0	0	_aMachine learning for healthcare applications / _h[electronic resource] _cEdited by Sachi Nandan Mohanty, G. Nalinipriya, Om Prakash Jena, Achyuth Sarkar.
250			_a1
264		1	_aHoboken, NJ : _bWiley, _c2021.
300			_a1 online resource (xx, 389 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _2rdaft
347			_bPDF
520			_aWhen considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.
650		4	_aArtificial Intelligence
650		4	_aMedical Informatics & Biomedical Information Technology
650		4	_aIntelligent Systems & Agents
655		0	_aElectronic books _92032
700	1		_aMohanty, Sachi Nandan _eeditor
700	1		_aNalinipriya, G. _eeditor
700	1		_aJena, Om Prakash _eeditor
700	1		_aSarkar, Achyuth _eeditor
776	0	8	_iPrinted edition: _z9781119791812
856	4	0	_uhttps://doi.org/10.1002/9781119792611 _aWiley Online Library
856	4	0	_uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781119792611 _aWiley Online Library
856	4	2	_3Cover Image _uhttps://onlinelibrary.wiley.com/cms/asset/5a85a169-02d4-4ae3-a606-ac9d56d7c734/9781119792611.cover.gif _aWiley Online Library
942			_2NLM _cEBK
999			_c200467354 _d85566