scholarly journals Special Issue on Computational Intelligence for Healthcare

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1841
Author(s):  
Gabriella Casalino ◽  
Giovanna Castellano
Keyword(s):  
Computational Intelligence ◽  
Health Data ◽  
Special Issue ◽  
Patient Health

The number of patient health data has been estimated to have reached 2314 exabytes by 2020 [...]

scholarly journals Special issue on The 17th Annual UK Workshop on Computational Intelligence

2018 ◽  
Vol 22 (21) ◽  
pp. 6965-6966
Author(s):  
Zhang Qingfu ◽  
Chao Fei
Keyword(s):  
Computational Intelligence ◽  
Special Issue

scholarly journals Introduction to the Special Issue “Advances in Computational Intelligence Applications in the Mining Industry”

Minerals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 67
Author(s):  
Rajive Ganguli ◽  
Sean Dessureault ◽  
Pratt Rogers
Keyword(s):  
Computational Intelligence ◽  
Mining Industry ◽  
Special Issue ◽  
Exciting Time

This is an exciting time for the mining industry, as it is on the cusp of a change in efficiency as it gets better at leveraging data [...]

Big data or big risk: general practitioner, practice nurse and practice manager attitudes to providing de-identified patient health data from electronic medical records to researchers

2020 ◽  
Vol 26 (6) ◽  
pp. 466
Author(s):  
Timothy Monaghan ◽  
Jo-Anne Manski-Nankervis ◽  
Rachel Canaway
Keyword(s):  
Primary Care ◽  
Health Information ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Health Data ◽  
Positive Effects ◽  
Patient Consent ◽  
Patient Health ◽  
General Practices ◽  
Patient Health Information

Research utilising de-identified patient health information extracted from electronic medical records (EMRs) from general practices has steadily grown in recent years in response to calls to increase use of health data for research and other secondary purposes in Australia. Little is known about the views of key primary care personnel on this issue, which are important, as they may influence whether practices agree to provide EMR data for research. This exploratory qualitative study investigated the attitudes and beliefs of general practitioners (GPs), practice managers (PMs) and practice nurses (PNs) around sharing de-identified EMR patient health information with researchers. Semi-structured interviews were conducted with 11 participants (6 GPs, 3 PMs and 2 PNs) recruited via purposive sampling from general practices in Victoria, Australia. Transcripts were coded and thematically analysed. Participants were generally enthusiastic about research utilising de-identified health information extracted from EMRs for altruistic reasons, including: positive effects on primary care research, clinical practice and population health outcomes. Concerns raised included patient privacy and data breaches, third-party use of extracted data and patient consent. These findings can provide guidance to researchers and policymakers in designing and implementing projects involving de-identified health information extracted from EMRs.

Privacy and Security: Are Your Health Information Systems up to Standard?

1996 ◽  
Vol 26 (4) ◽  
pp. 197-201 ◽  
Author(s):  
Tina Magennis ◽  
Jennifer Mitchell
Keyword(s):  
Health Care ◽  
Information Systems ◽  
Health Information ◽  
Health Information Systems ◽  
Health Data ◽  
Human Behaviour ◽  
Personal Health ◽  
Privacy And Security ◽  
Patient Health ◽  
Privacy And Confidentiality

As electronic patient health information systems become more fully developed and widespread, there are persistent concerns about the privacy and confidentiality of the personal health data being stored and disseminated. Standards Australia has released two Standards which provide useful guidelines for the organisational, technological and human behaviour solutions required to protect privacy and confidentiality in health care organisations. The major requirements of these Standards are outlined and the implications of the Standards for health information managers are discussed.

Special Issue on Selected Papers WISP'99

2001 ◽  
Vol 5 (1) ◽  
pp. 1-1
Author(s):  
Annamária R. Várkonyi-Kóczy ◽  
Keyword(s):  
Signal Processing ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Large Scale ◽  
International Workshop ◽  
Processing System ◽  
Mixed Data ◽  
Special Issue ◽  
Excellent Research ◽  
Signal Processing Systems

Today's complex industrial and engineering systems - especially with the appearance of large-scale embedded and/or real-time systems - confront researchers and engineers with completely new challenges. Measurement and signal processing systems are involved in almost all kinds of activities in that field where control problems, system identification problems, industrial technologies, etc., are to be solved, i.e., when signals, parameters, or attributes must be measured, monitored, approximated, or determined somehow. In a large number of cases, traditional information processing tools and equipment fail to handle these problems. Not only is the handling of previously unseen spatial and temporal complexity questionable but such problems have also to be addressed such as the interaction and communication of subsystems based on entirely different modeling and information expression methods, the handling of abrupt changes within the environment and/or the processing system, the possible temporal shortage of computational power and/or loss of some data due to the former. Signal processing should even in these cases provide outputs of acceptable quality to continue the operation of the complete system, producing data for qualitative evaluations and supporting decisions. It means the introduction of new ideas for specifying, designing, implementing, and operating sophisticated signal processing systems. Intelligent - artificial intelligence, soft computing, anytime, etc. - methods are serious candidates for handling many theoretical and practical problems, providing a better description, and, in many cases, are the best if not the only alternatives for emphasizing significant aspects of system behavior. These techniques, however, are relatively new methods and up until now, not widely used in the field of signal processing because some of the critical questions related to design and verification are not answered properly and because uncertainty is maintained quite differently than in classical metrology. After the initiation of the 1999 IEEE International Workshop on Intelligent Signal Processing, WISP'99, which was the first event to start linking scientific communities working in the fields of intelligent systems and signal processing and hoping that it will attract more and more scientists and engineers in these hot topics, this special issue continues this pioneering work by offering a selection of nine papers fitting into the profile of the journal from the numerous high quality ones presented at WISP'99. These excellent papers deal with different aspects of advanced computational intelligence in signal processing, including the application of neural networks, fuzzy techniques, genetic and anytime algorithms in modeling, signal processing, noise cancellation, identification, and pattern recognition, multisensorial information fusion and intelligent classification in image processing, exact and nonexact complexity reduction, and nonclassical and mixed data and uncertainty representation and handling. As an editor of this special issue, I would like to express my thanks to all of the contributors and my belief in that the excellent research results it contains provide the basis for further strengthening and spreading of advanced computational intelligence in signal processing opening wide possibilities for new theoretical and practical achievements.

Selected Papers from ISCIIA 2010

2011 ◽  
Vol 15 (3) ◽  
pp. 321-321
Author(s):  
Yasufumi Takama
Keyword(s):  
Fuzzy Logic ◽  
Computational Intelligence ◽  
Web Mining ◽  
Review Process ◽  
Industrial Applications ◽  
Future Research ◽  
Heilongjiang Province ◽  
Special Issue ◽  
Advanced Technologies ◽  
New Challenges

The 4th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2010), held at the Harbin University of Science and Technology in Heilongjiang Province, China, in August 2010 focused on advanced technologies for computational intelligence and industrial applications. A series of ISCIIA symposiums has provided a unique opportunity for the academic and industrial communities to address new challenges, share new-found solutions, and discuss directions for future research. Of the ISCIIA2010fs 50 papers, 11 outstanding papers have been selected for this special issue after fair and strict review process. Among this issuefs topics are fuzzy logic,Web mining, Kansei Information Processing (KIP), brain informatics, and human-centered systems. Given the importance of these topics to both the academic and industrial communities, this issue should contribute much to active exchange between both communities. As the Editor of this special issue, I thank all of the contributors and reviewers for their time and cooperation. Herefs hoping that the next ISCIIA, which is being held in Hokkaido, Japan, in 2012, will be as successful and fruitful as the 2010 symposium has been.

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