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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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Salvato in:
Natura: Online
Lingua:inglese
Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute 2022
Soggetti:
sEMG
deep learning
neural networks
gait phase
classification
everyday walking
convolutional neural network
CRISPR
leukemia nucleus image
segmentation
soft covering rough set
clustering
machine learning algorithm
soft computing
multistage support vector machine model
multiple imputation by chained equations
SVM-based recursive feature elimination
unipolar depression
diabetic retinopathy (DR)
pre-trained deep ConvNet
uni-modal deep features
multi-modal deep features
transfer learning
1D pooling
cross pooling
IMU
gait analysis
long-term monitoring
multi-unit
multi-sensor
time synchronization
Internet of Medical Things
body area network
MIMU
early detection
sepsis
evaluation metrics
machine learning
medical informatics
feature extraction
physionet challenge
electrocardiogram
Premature ventricular contraction
sparse autoencoder
unsupervised learning
Softmax regression
medical diagnosis
artificial neural network
e-health
Tri-Fog Health System
fault data elimination
health status prediction
health status detection
health off
diffusion tensor imaging
ensemble learning
decision support systems
healthcare
computational intelligence
Alzheimer’s disease
fuzzy inference systems
genetic algorithms
next-generation sequencing
ovarian cancer
interpretable models
n/a
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
Accesso online:ONIX_20220111_9783036523774_803
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ONIX_20220111_9783036523774_803

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