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...
Saved in:
HerausgeberIn: | |
---|---|
Sonstige: | |
Year of Publication: | 2021 |
Language: | English |
Physical Description: | 1 electronic resource (226 p.) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
993545493604498 |
---|---|
ctrlnum |
(CKB)5400000000042630 (oapen)https://directory.doabooks.org/handle/20.500.12854/76971 (EXLCZ)995400000000042630 |
collection |
bib_alma |
record_format |
marc |
spelling |
Castellano, Giovanna edt Computational Intelligence in Healthcare Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 1 electronic resource (226 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. English Information technology industries bicssc 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 3-0365-2377-4 3-0365-2378-2 Casalino, Gabriella edt Castellano, Giovanna oth Casalino, Gabriella oth |
language |
English |
format |
eBook |
author2 |
Casalino, Gabriella Castellano, Giovanna Casalino, Gabriella |
author_facet |
Casalino, Gabriella Castellano, Giovanna Casalino, Gabriella |
author2_variant |
g c gc g c gc |
author2_role |
HerausgeberIn Sonstige Sonstige |
title |
Computational Intelligence in Healthcare |
spellingShingle |
Computational Intelligence in Healthcare |
title_full |
Computational Intelligence in Healthcare |
title_fullStr |
Computational Intelligence in Healthcare |
title_full_unstemmed |
Computational Intelligence in Healthcare |
title_auth |
Computational Intelligence in Healthcare |
title_new |
Computational Intelligence in Healthcare |
title_sort |
computational intelligence in healthcare |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
physical |
1 electronic resource (226 p.) |
isbn |
3-0365-2377-4 3-0365-2378-2 |
illustrated |
Not Illustrated |
work_keys_str_mv |
AT castellanogiovanna computationalintelligenceinhealthcare AT casalinogabriella computationalintelligenceinhealthcare |
status_str |
n |
ids_txt_mv |
(CKB)5400000000042630 (oapen)https://directory.doabooks.org/handle/20.500.12854/76971 (EXLCZ)995400000000042630 |
carrierType_str_mv |
cr |
is_hierarchy_title |
Computational Intelligence in Healthcare |
author2_original_writing_str_mv |
noLinkedField noLinkedField noLinkedField |
_version_ |
1759318884864753664 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04800nam-a2201105z--4500</leader><controlfield tag="001">993545493604498</controlfield><controlfield tag="005">20230221123451.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202201s2021 xx |||||o ||| eneng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)5400000000042630</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/76971</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)995400000000042630</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Castellano, Giovanna</subfield><subfield code="4">edt</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational Intelligence in Healthcare</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Basel, Switzerland</subfield><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (226 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Information technology industries</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">sEMG</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">deep learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">neural networks</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gait phase</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">classification</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">everyday walking</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">convolutional neural network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">CRISPR</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">leukemia nucleus image</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">segmentation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">soft covering rough set</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">clustering</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">machine learning algorithm</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">soft computing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multistage support vector machine model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multiple imputation by chained equations</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">SVM-based recursive feature elimination</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">unipolar depression</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">diabetic retinopathy (DR)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">pre-trained deep ConvNet</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">uni-modal deep features</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multi-modal deep features</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">transfer learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">1D pooling</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cross pooling</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">IMU</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gait analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">long-term monitoring</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multi-unit</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multi-sensor</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">time synchronization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Internet of Medical Things</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">body area network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">MIMU</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">early detection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">sepsis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">evaluation metrics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">medical informatics</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature extraction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">physionet challenge</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">electrocardiogram</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Premature ventricular contraction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">sparse autoencoder</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">unsupervised learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Softmax regression</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">medical diagnosis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">artificial neural network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">e-health</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Tri-Fog Health System</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">fault data elimination</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">health status prediction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">health status detection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">health off</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">diffusion tensor imaging</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">ensemble learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">decision support systems</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">healthcare</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">computational intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Alzheimer’s disease</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">fuzzy inference systems</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">genetic algorithms</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">next-generation sequencing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">ovarian cancer</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">interpretable models</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">n/a</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-0365-2377-4</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-0365-2378-2</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Casalino, Gabriella</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Castellano, Giovanna</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Casalino, Gabriella</subfield><subfield code="4">oth</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2023-03-03 03:20:34 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2022-04-04 09:22:53 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5337930640004498&Force_direct=true</subfield><subfield code="Z">5337930640004498</subfield><subfield code="8">5337930640004498</subfield></datafield></record></collection> |