Reprint
Big Data for eHealth Applications
Edited by
September 2022
212 pages
- ISBN978-3-0365-5053-4 (Hardback)
- ISBN978-3-0365-5054-1 (PDF)
This book is a reprint of the Special Issue Big Data for eHealth Applications that was published in
Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
This Special Issue is focused on technologies and methodologies in the field of big data analytics for eHealth, also considering their combined use with Internet of Thing (IoT) devices, artificial intelligence (AI) methods, and cyber security (CS) techniques for the definition of complex systems and architectures for the eHealth domain. Papers devoted to the analysis of COVID-19-related big data are also considered.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
breast cancer; data mining; machine learning; feature selection; over-sampling; class imbalance; beats classification; ECG; algorithms; ML; random forest; copula; mutual information; classification; COVID-19; federated learning; deep learning; artificial intelligence; healthcare; data privacy-preserving; artificial intelligence; chronic heart failure; cardiogenic pulmonary edema; machine learning; neural networks; e-health; Arduino; wearable; machine learning; BERT; COVID-19; scientific text classification; transfer learning; scientific publications; deep learning; risk management; decision-making; Spatial Multicriteria Analysis; temporal analysis; vulnerability risk; COVID-19; healthcare ecosystem; medical devices; cyberattack path; vulnerability; exploitability; biomedical NER; corpus annotation; distant supervision; active learning; deep learning; n/a