LEADER 03969nam 22005535 450 001 9910739480903321 005 20251113174716.0 010 $a3-030-99728-6 024 7 $a10.1007/978-3-030-99728-1 035 $a(MiAaPQ)EBC7029008 035 $a(Au-PeEL)EBL7029008 035 $a(CKB)24148051900041 035 $a(PPN)263900266 035 $a(OCoLC)1336590528 035 $a(DE-He213)978-3-030-99728-1 035 $a(EXLCZ)9924148051900041 100 $a20220701d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Bioscience and Biosystems for Detection and Management of Diabetes /$fedited by Kishor Kumar Sadasivuni, John-John Cabibihan, Abdulaziz Khalid A M Al-Ali, Rayaz A. Malik 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (313 pages) 225 1 $aSpringer Series on Bio- and Neurosystems,$x2520-8543 ;$v13 311 08$aPrint version: Sadasivuni, Kishor Kumar Advanced Bioscience and Biosystems for Detection and Management of Diabetes Cham : Springer International Publishing AG,c2022 9783030997274 327 $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. 330 $aThis book covers the medical condition of diabetic patients, their early symptoms and methods conventionally used for diagnosing and monitoring diabetes. It describes various techniques and technologies used for diabetes detection. The content is built upon moving from regressive technology (invasive) and adapting new-age pain-free technologies (non-invasive), machine learning and artificial intelligence for diabetes monitoring and management. This book details all the popular technologies used in the health care and medical fields for diabetic patients. An entire chapter is dedicated to how the future of this field will be shaping up and the challenges remaining to be conquered. Finally, it shows artificial intelligence and predictions, which can be beneficial for the early detection, dose monitoring and surveillance for patients suffering from diabetes. 410 0$aSpringer Series on Bio- and Neurosystems,$x2520-8543 ;$v13 606 $aBiomedical engineering 606 $aMachine learning 606 $aBiomedical Engineering and Bioengineering 606 $aMachine Learning 615 0$aBiomedical engineering. 615 0$aMachine learning. 615 14$aBiomedical Engineering and Bioengineering. 615 24$aMachine Learning. 676 $a616.462 676 $a616.462075 702 $aSadasivuni$b Kishor Kumar$f1986- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739480903321 996 $aAdvanced bioscience and biosystems for detection and management of diabetes$93553255 997 $aUNINA