000			04175nam a22003975i 4500
001			394688
003			ES-MaUEC
005			20230102123151.0
007			cr nn 008mamaa
008			220706s2022 si | s |||| 0|eng d
020			_a9789811920578
024	7		_a10.1007/978-981-19-2057-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aPrognostic Models in Healthcare: AI and Statistical Approaches _cedited by Tanzila Saba, Amjad Rehman, Sudipta Roy
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXII, 504 páginas) _b211 ilustraciones, 161 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aStudies in Big Data _x2197-6511 _v109
505	0		_aSegmentation of White Blood Cells in Acute Myeloid Leukaemia Microscopic Images: The Current Challenges and Future Solutions -- Computer Vision Based Prognostic Modeling of COVID-19 from Medical Imaging -- Skin Lesion Classification From Dermoscopic Images with Deep Residual Network based Fused Pigmented Deep Feature Extraction and Entropy Based Best Features Selection Approach -- Computer Vision Technologies for COVID-19 Prediction, Diagnosis and Prevention -- Health monitoring methods in heart diseases based on data mining approach, a directional survey -- Machine learning based brain diseases diagnosing in electroencephalogram signals, Alzheimer and Parkinson's -- Skin Lesion Detection Using Recent Machine Learning Approaches -- Improving monitoring and controling parameters for Alzheimer's patients based on IoT -- A Novel Method for Lung Segmentation of Chest with Convolutional Neural Network -- Leukemia Detection Using Machine and Deep Learning Through Microscopic Images-A Review.
520			_aThis book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints. Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.
700	1		_aSaba, Tanzila _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aRehman, Amjad _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aRoy, Sudipta _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9789811920561
776	0	8	_iPrinted edition: _z9789811920585
776	0	8	_iPrinted edition: _z9789811920592
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-2057-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Robotics_2022
999			_c394688 _d394688