000			02111cam a2200301zu 4500
001			88955464
003			FRCYB88955464
005			20250108004134.0
006			m o d
007			cr un
008			250108s2020 fr | o|||||0|0|||eng d
020			_a9780128193143
035			_aFRCYB88955464
040			_aFR-PaCSA _ben _c _erda
100	1		_aLee, Kun Chang
245	0	1	_aData Analytics in Biomedical Engineering and Healthcare _c['Lee, Kun Chang', 'Roy, Sanjiban Sekhar', 'Samui, Pijush']
264		1	_bElsevier Science _c2020
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aLee, Kun Chang
700	0		_aRoy, Sanjiban Sekhar
700	0		_aSamui, Pijush
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88955464 _qtext/html _a
520			_aData Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks
999			_c79260 _d79260