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220822 ||| eng |
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|a 9783036523774
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|a books978-3-0365-2378-1
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|a 9783036523781
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| 100 |
1 |
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|a Castellano, Giovanna
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| 245 |
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|a Computational Intelligence in Healthcare
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (226 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a e-health
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| 653 |
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|a multistage support vector machine model
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| 653 |
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|a CRISPR
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| 653 |
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|a health status detection
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| 653 |
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|a soft computing
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| 653 |
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|a soft covering rough set
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| 653 |
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|a body area network
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| 653 |
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|a next-generation sequencing
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| 653 |
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|a uni-modal deep features
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| 653 |
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|a Tri-Fog Health System
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| 653 |
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|a MIMU
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| 653 |
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|a n/a
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| 653 |
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|a diabetic retinopathy (DR)
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| 653 |
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|a pre-trained deep ConvNet
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| 653 |
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|a deep learning
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| 653 |
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|a cross pooling
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| 653 |
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|a neural networks
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| 653 |
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|a fuzzy inference systems
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| 653 |
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|a multiple imputation by chained equations
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| 653 |
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|a physionet challenge
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| 653 |
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|a multi-unit
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| 653 |
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|a health off
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| 653 |
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|a multi-modal deep features
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| 653 |
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|a unsupervised learning
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| 653 |
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|a long-term monitoring
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| 653 |
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|a machine learning algorithm
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| 653 |
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|a decision support systems
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| 653 |
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|a feature extraction
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| 653 |
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|a ensemble learning
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| 653 |
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|a artificial neural network
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| 653 |
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|a health status prediction
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| 653 |
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|a medical diagnosis
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| 653 |
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|a transfer learning
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| 653 |
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|a interpretable models
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| 653 |
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|a Information technology industries / bicssc
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| 653 |
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|a multi-sensor
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| 653 |
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|a diffusion tensor imaging
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| 653 |
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|a gait phase
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| 653 |
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|a Softmax regression
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| 653 |
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|a clustering
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| 653 |
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|a unipolar depression
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| 653 |
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|a evaluation metrics
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| 653 |
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|a 1D pooling
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| 653 |
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|a genetic algorithms
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| 653 |
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|a early detection
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| 653 |
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|a IMU
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| 653 |
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|a SVM-based recursive feature elimination
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| 653 |
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|a segmentation
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| 653 |
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|a ovarian cancer
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| 653 |
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|a Internet of Medical Things
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| 653 |
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|a time synchronization
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| 653 |
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|a medical informatics
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| 653 |
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|a classification
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| 653 |
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|a healthcare
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| 653 |
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|a computational intelligence
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| 653 |
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|a fault data elimination
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| 653 |
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|a Alzheimer's disease
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| 653 |
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|a leukemia nucleus image
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| 653 |
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|a convolutional neural network
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| 653 |
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|a sepsis
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| 653 |
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|a Premature ventricular contraction
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| 653 |
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|a electrocardiogram
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| 653 |
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|a sparse autoencoder
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| 653 |
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|a gait analysis
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| 653 |
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|a sEMG
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| 653 |
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|a everyday walking
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| 700 |
1 |
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|a Casalino, Gabriella
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| 700 |
1 |
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|a Castellano, Giovanna
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| 700 |
1 |
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|a Casalino, Gabriella
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.3390/books978-3-0365-2378-1
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/76971
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/4563
|7 0
|x Verlag
|3 Volltext
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| 082 |
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|a 000
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|a 610
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|a 700
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|a 600
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|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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