Computational Intelligence in Healthcare

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...

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Other Authors: Castellano, Giovanna (Editor), Casalino, Gabriella (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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041 0 |a English 
042 |a dc 
072 7 |a KNTX  |2 bicssc 
100 1 |a Castellano, Giovanna  |4 edt 
700 1 |a Casalino, Gabriella  |4 edt 
700 1 |a Castellano, Giovanna  |4 oth 
700 1 |a Casalino, Gabriella  |4 oth 
245 1 0 |a Computational Intelligence in Healthcare 
260 |a Basel, Switzerland  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2021 
300 |a 1 electronic resource (226 p.) 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a 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. 
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546 |a English 
650 7 |a Information technology industries  |2 bicssc 
653 |a sEMG 
653 |a deep learning 
653 |a neural networks 
653 |a gait phase 
653 |a classification 
653 |a everyday walking 
653 |a convolutional neural network 
653 |a CRISPR 
653 |a leukemia nucleus image 
653 |a segmentation 
653 |a soft covering rough set 
653 |a clustering 
653 |a machine learning algorithm 
653 |a soft computing 
653 |a multistage support vector machine model 
653 |a multiple imputation by chained equations 
653 |a SVM-based recursive feature elimination 
653 |a unipolar depression 
653 |a diabetic retinopathy (DR) 
653 |a pre-trained deep ConvNet 
653 |a uni-modal deep features 
653 |a multi-modal deep features 
653 |a transfer learning 
653 |a 1D pooling 
653 |a cross pooling 
653 |a IMU 
653 |a gait analysis 
653 |a long-term monitoring 
653 |a multi-unit 
653 |a multi-sensor 
653 |a time synchronization 
653 |a Internet of Medical Things 
653 |a body area network 
653 |a MIMU 
653 |a early detection 
653 |a sepsis 
653 |a evaluation metrics 
653 |a machine learning 
653 |a medical informatics 
653 |a feature extraction 
653 |a physionet challenge 
653 |a electrocardiogram 
653 |a Premature ventricular contraction 
653 |a sparse autoencoder 
653 |a unsupervised learning 
653 |a Softmax regression 
653 |a medical diagnosis 
653 |a artificial neural network 
653 |a e-health 
653 |a Tri-Fog Health System 
653 |a fault data elimination 
653 |a health status prediction 
653 |a health status detection 
653 |a health off 
653 |a diffusion tensor imaging 
653 |a ensemble learning 
653 |a decision support systems 
653 |a healthcare 
653 |a computational intelligence 
653 |a Alzheimer's disease 
653 |a fuzzy inference systems 
653 |a genetic algorithms 
653 |a next-generation sequencing 
653 |a ovarian cancer 
653 |a interpretable models 
653 |a n/a 
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