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020			_a9783030997281
024	7		_a10.1007/978-3-030-99728-1 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
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245	1	0	_aAdvanced Bioscience and Biosystems for Detection and Management of Diabetes _h[electronic resource] / _cedited by Kishor Kumar Sadasivuni, John-John Cabibihan, Abdulaziz Khalid A M Al-Ali, Rayaz A. Malik.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_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.
650		0	_aBiomedical engineering.
650		0	_aMachine learning.
650	1	4	_aBiomedical Engineering and Bioengineering.
650	2	4	_aMachine Learning.
653		0	_aDiabetes Mellitus -- diagnosis
653		0	_aDiabetes Mellitus -- therapy
700	1		_aSadasivuni, Kishor Kumar. _eeditor. _0(orcid)0000-0003-2730-6483 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aCabibihan, John-John. _eeditor. _0(orcid)0000-0001-5892-743X _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aA M Al-Ali, Abdulaziz Khalid. _eeditor. _0(orcid)0000-0003-0006-2642 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMalik, Rayaz A. _eeditor. _0(orcid)0000-0002-7188-8903 _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
830		0	_aSpringer Series on Bio- and Neurosystems, _x2520-8543 ; _v13
856	4	0	_uhttps://doi.org/10.1007/978-3-030-99728-1 _3Springer eBooks _zOnline access link to the resource
942			_2NLM _cEBK