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008			220701s2022 sz a o |||| 0|eng d
020			_a9783030997281
024	7		_a10.1007/978-3-030-99728-1 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC660 _b2022 EB
245	0	0	_aAdvanced Bioscience and Biosystems for Detection and Management of Diabetes _cedited by Kishor Kumar Sadasivuni, John-John Cabibihan, Abdulaziz Khalid A M Al-Ali, Rayaz A. Malik
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VIII, 313 páginas) _b150 ilustraciones, 148 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringer Series on Bio- and Neurosystems _x2520-8543 _v13
505	0		_aIntroduction -- Review of Emerging Approaches Utilizing Alternative Physiological Human Body Fluids in Non- or Minimally Invasive Glucose Monitoring -- Current Status of Non-invasive Diabetics Monitoring -- A New Solution for Non-invasive Glucose Measurement Based on Heart Rate Variability -- Optics Based Techniques for Monitoring Diabetics -- SPR Assisted Diabetics Detection -- Infrared and Raman Spectroscopy Assisted Diagnosis of Diabetics -- Photoacoustic Spectroscopy Mediated Non-Invasive Detection of Diabetics -- Electrical Bioimpedance Based Estimation of Diabetics -- Millimeter and Microwave Sensing Technique for Diagnosis of Diabetics -- Different Machine Learning Algorithm involved in Glucose Monitoring to Prevent Diabetes Complications and Enhanced Diabetes Mellitus Management -- The role of Artificial Intelligence in Diabetes management -- Artificial Intelligence and Machine learning for Diabetes Decision Support -- Commercial Non-Invasive Glucose Sensor Devices for Monitoring Diabetics -- Future Developments in Invasive and Non-Invasive Diabetics Monitoring.
520			_aThis book covers the medical condition of diabetic patients, their early symptoms and methods conventionally used for diagnosing and monitoring diabetes. It describes various techniques and technologies used for diabetes detection. The content is built upon moving from regressive technology (invasive) and adapting new-age pain-free technologies (non-invasive), machine learning and artificial intelligence for diabetes monitoring and management. This book details all the popular technologies used in the health care and medical fields for diabetic patients. An entire chapter is dedicated to how the future of this field will be shaping up and the challenges remaining to be conquered. Finally, it shows artificial intelligence and predictions, which can be beneficial for the early detection, dose monitoring and surveillance for patients suffering from diabetes.
988			_aSpringer_Engineering_2022
650		7	_2embne _9138582 _aDiabetes
700	1		_aSadasivuni, Kishor Kumar _eeditor literario _0(orcid)0000-0003-2730-6483 _1https://orcid.org/0000-0003-2730-6483 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aCabibihan, John-John _eeditor literario _0(orcid)0000-0001-5892-743X _1https://orcid.org/0000-0001-5892-743X _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aA M Al-Ali, Abdulaziz Khalid _eeditor literario _0(orcid)0000-0003-0006-2642 _1https://orcid.org/0000-0003-0006-2642 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMalik, Rayaz A. _eeditor literario _0(orcid)0000-0002-7188-8903 _1https://orcid.org/0000-0002-7188-8903 _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030997274
776	0	8	_iPrinted edition: _z9783030997298
776	0	8	_iPrinted edition: _z9783030997304
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-99728-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eb _zSI