LEADER 04928nam 2201129z- 450 001 9910674044803321 005 20231214133525.0 035 $a(CKB)5400000000042630 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76971 035 $a(EXLCZ)995400000000042630 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Intelligence in Healthcare 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (226 p.) 311 $a3-0365-2377-4 311 $a3-0365-2378-2 330 $aThe number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications. 606 $aInformation technology industries$2bicssc 610 $asEMG 610 $adeep learning 610 $aneural networks 610 $agait phase 610 $aclassification 610 $aeveryday walking 610 $aconvolutional neural network 610 $aCRISPR 610 $aleukemia nucleus image 610 $asegmentation 610 $asoft covering rough set 610 $aclustering 610 $amachine learning algorithm 610 $asoft computing 610 $amultistage support vector machine model 610 $amultiple imputation by chained equations 610 $aSVM-based recursive feature elimination 610 $aunipolar depression 610 $adiabetic retinopathy (DR) 610 $apre-trained deep ConvNet 610 $auni-modal deep features 610 $amulti-modal deep features 610 $atransfer learning 610 $a1D pooling 610 $across pooling 610 $aIMU 610 $agait analysis 610 $along-term monitoring 610 $amulti-unit 610 $amulti-sensor 610 $atime synchronization 610 $aInternet of Medical Things 610 $abody area network 610 $aMIMU 610 $aearly detection 610 $asepsis 610 $aevaluation metrics 610 $amachine learning 610 $amedical informatics 610 $afeature extraction 610 $aphysionet challenge 610 $aelectrocardiogram 610 $aPremature ventricular contraction 610 $asparse autoencoder 610 $aunsupervised learning 610 $aSoftmax regression 610 $amedical diagnosis 610 $aartificial neural network 610 $ae-health 610 $aTri-Fog Health System 610 $afault data elimination 610 $ahealth status prediction 610 $ahealth status detection 610 $ahealth off 610 $adiffusion tensor imaging 610 $aensemble learning 610 $adecision support systems 610 $ahealthcare 610 $acomputational intelligence 610 $aAlzheimer's disease 610 $afuzzy inference systems 610 $agenetic algorithms 610 $anext-generation sequencing 610 $aovarian cancer 610 $ainterpretable models 615 7$aInformation technology industries 700 $aCastellano$b Giovanna$4edt$01339017 702 $aCasalino$b Gabriella$4edt 702 $aCastellano$b Giovanna$4oth 702 $aCasalino$b Gabriella$4oth 906 $aBOOK 912 $a9910674044803321 996 $aComputational Intelligence in Healthcare$93059521 997 $aUNINA