LEADER 04573nam 2201045z- 450 001 9910743269303321 005 20230911 035 $a(CKB)5690000000228615 035 $a(oapen)doab113912 035 $a(EXLCZ)995690000000228615 100 $a20230920c2023uuuu -u- - 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aHealth and Public Health Applications for Decision Support Using Machine Learning 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2023 215 $a1 online resource (214 p.) 311 08$a3-0365-8548-6 330 $a"Health and Public Health Applications for Decision Support Using Machine Learning" is a reprint that explores the intersection of machine learning and health sciences. It presents a collection of research and innovations showcasing how data-driven algorithms can transform patient care, disease diagnosis, and public health management. The reprint covers a wide range of topics, including natural language processing for biomedical relation extraction, ensemble learning for blood glucose level forecasting in diabetes management, machine learning for predicting walking stability and fall risk among the elderly, deep learning for pneumonia-infected lung volume quantification, and more.The reprint also discusses applications in precision medicine, early detection of renal damage, cardiac health monitoring, stress classification for mental health assessment, and early diagnosis of intracranial internal carotid artery stenosis. It emphasizes the role of machine learning in managing health crises, such as COVID-19 detection using ECG, voice, and X-ray systems, and reviews AI models in diagnosing adult-onset dementia disorders.Overall, this reprint aims to inspire researchers and healthcare professionals by showcasing the transformative potential of machine learning in healthcare. It hopes to encourage further research and collaboration to advance healthcare and technological innovations for a healthier future. 610 $aaction units 610 $aadult-onset dementia 610 $aAlzheimer's disease 610 $aartificial intelligence 610 $aartificial neural network 610 $aatherosclerosis 610 $aaudio visual 610 $ablood glucose 610 $aCardiac health 610 $aChemProt 610 $acolonies 610 $acomparison between manual and automated image segmentation 610 $acomputerized diagnostic systems 610 $aconvolutional neural network 610 $aCOVID-19 610 $aCOVID-19 detection 610 $aCOVID-19 severity assessment 610 $aCPR (chemical-protein relation) 610 $aCVD classification 610 $adata selection 610 $aDDI (drug-drug interaction) 610 $adeep learning 610 $adeep neural network 610 $adiabetes 610 $adiscrimination 610 $aDoppler ultrasound 610 $aECG 610 $aemotion 610 $aensemble learning 610 $agait 610 $aGAT (graph-attention network) 610 $agroup-based trajectory modeling 610 $ahemodynamic modeling 610 $aimage processing 610 $ainfected lung segmentation 610 $ainternal carotid artery 610 $alargest Lyapunov exponent (LyE) 610 $amachine learning 610 $amachine-learning models 610 $amagnetic resonance imaging 610 $aMeasurement uncertainty 610 $aMonte Carlo method 610 $amovement synergy 610 $an/a 610 $aneural networks 610 $aneuromuscular control 610 $aoverground walking 610 $apetri-plates 610 $apretrained model 610 $aprincipal component analysis (PCA) 610 $aquantification of lung disease severity 610 $arelation extraction 610 $arisk assessment tool 610 $aRNN-LSTM 610 $ascreening strategy 610 $aself-attention 610 $asignal processing 610 $aspeech 610 $astress 610 $astroke 610 $asubclinical renal damage 610 $aT5 (text-to-text transfer transformer) 610 $atime-series forecasting 610 $atransfer learning 610 $atransformer 906 $aBOOK 912 $a9910743269303321 996 $aHealth and Public Health Applications for Decision Support Using Machine Learning$93560493 997 $aUNINA