Computational Intelligence in Healthcare

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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Bibliographic Details
Other Authors: Castellano, Giovanna (Editor), Casalino, Gabriella (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
Information technology industries
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
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Summary: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.
Physical Description:1 electronic resource (226 p.)
ISBN:books978-3-0365-2378-1
9783036523774
9783036523781
Access:Open Access

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