04965nam 2201153z- 450 991067404480332120220111(CKB)5400000000042630(oapen)https://directory.doabooks.org/handle/20.500.12854/76971(oapen)doab76971(EXLCZ)99540000000004263020202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierComputational Intelligence in HealthcareBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (226 p.)3-0365-2377-4 3-0365-2378-2 The 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.Information technology industriesbicssc1D poolingAlzheimer's diseaseartificial neural networkbody area networkclassificationclusteringcomputational intelligenceconvolutional neural networkCRISPRcross poolingdecision support systemsdeep learningdiabetic retinopathy (DR)diffusion tensor imaginge-healthearly detectionelectrocardiogramensemble learningevaluation metricseveryday walkingfault data eliminationfeature extractionfuzzy inference systemsgait analysisgait phasegenetic algorithmshealth offhealth status detectionhealth status predictionhealthcareIMUInternet of Medical Thingsinterpretable modelsleukemia nucleus imagelong-term monitoringmachine learningmachine learning algorithmmedical diagnosismedical informaticsMIMUmulti-modal deep featuresmulti-sensormulti-unitmultiple imputation by chained equationsmultistage support vector machine modeln/aneural networksnext-generation sequencingovarian cancerphysionet challengepre-trained deep ConvNetPremature ventricular contractionsegmentationsEMGsepsissoft computingsoft covering rough setSoftmax regressionsparse autoencoderSVM-based recursive feature eliminationtime synchronizationtransfer learningTri-Fog Health Systemuni-modal deep featuresunipolar depressionunsupervised learningInformation technology industriesCastellano Giovannaedt1339017Casalino GabriellaedtCastellano GiovannaothCasalino GabriellaothBOOK9910674044803321Computational Intelligence in Healthcare3059521UNINA