The recent computational intelligence (CI) revolution is changing the way we work, communicate, and conduct research. It also plays an important role in the advancement of health and medicine by providing innovative technological solutions in healthcare. However, we are overwhelmed by Big Data from the internet and other issues like Large Language Model (LLM) hallucinations. To address these challenges, we propose to organize a special session dedicated to "Advances in Computational Intelligence in Health and Medicine (ACIHM)" at the IEEE WCCI 2024 conference. This special session aims to explore advancements in health research and clinical practice by concentrating on the most recent advancements and uses of CI in health and medicine that are shaping the future of healthcare. The focus of this special session is the application of CI methods (including machine learning, deep learning, and pattern recognition) in the fields of bioinformatics, drug discovery, personalized medicine, and biomedical engineering. We want to bridge the gap between scientific communities in CI and clinical experts in health and medicine from diverse multidisciplinary backgrounds, fostering collaboration and the exchange of ideas. Cross-fertilization of the three technical disciplines and newly emerging research areas are strongly encouraged. We will accept a wide range of topics (different knowledge and technologies) in this interdisciplinary field. We encourage submissions of original papers that are unpublished original work for this special session at IEEE WCCI2024 that demonstrates the clinical application of CI to transform healthcare and medicine. The topics are, but not limited to, the following:
Medical Image Analysis
- Medical image analysis and segmentation
- Medical imaging and pattern recognition
- Feature extraction
- Diagnosis using medical imaging modalities: MRI, CT scans, X-rays, and microscopy
Disease Prediction and Diagnosis
- Medical image segmentation
- Predictive modeling
- Disease diagnosis
- Risk assessment using CI approaches
- Medical data classification
- Fuzzy logic-based techniques
- Bayesian-based techniques
- Multiobjective data mining
- Predictive fitness landscape design
- Many-objective optimization
Healthcare Decision Support Systems
- Diagnostic decision-making
- Personalized treatment recommendations
- Patient care optimization
Patient Monitoring and Wearable Devices
- Innovations in wearable technology
- Real-time patient monitoring
- Disease management
- Telemedicine applications
- Wearable devices
- Mobile technologies for healthcare applications
Drug Discovery and Pharmacology
- Drug design
- Virtual screening
- Pharmacokinetics
- Toxicity prediction
- Development of new pharmaceuticals
- Personalized medicine
Bioinformatics and Genomic Data Analysis
- Computational proteomics
- Emergent properties in complex biological systems
- Omics data analysis
- Gene finding
- Genetic networks
- In-silico optimization of biological systems
- Metabolic pathway analysis
- Molecular docking and drug design
- Molecular sequence alignment and analysis
- Motif and signal detection
- Single nucleotide polymorphism
- Biomarker detection
Single and multi-objective optimization techniques for Bioinformatics and Bioengineering problems
- Evolutionary algorithms
- Swarm Intelligence
- Metaheuristics
- Fuzzy optimization
- Hybrid optimization algorithms (combinations of heuristics and exact methods)
Special session papers should be uploaded online through the paper submission website of IEEE WCCI 2024. Please select the corresponding special session name (“Special Session on Advances in Computational Intelligence in Health and Medicine”) as the “main research topic” in the submission. Further information about WCCI 2024 can be found at https://wcci2024.org/
Assistant Professor, Thompson Rivers University, BC, Canada
Visiting Scientist, Princess Margaret Cancer Centre, Toronto, Canada
https://www.mamatjanlab.com/IEEE-CIHM/
Dr. Mamatjan is an Assistant Professor at Thompson Rivers University in Canada and a Visiting Scientist at the University Health Network (UHN) / Princess Margaret Cancer Centre in Toronto. He obtained his Ph.D. degree in Engineering from the University of Glamorgan in the UK, in 2009. He worked as a Marie Curie Research Fellow at Philips Research Lab in Germany. His research interest lies in developing smart patient monitoring systems and cancer diagnostic tools using artificial intelligence techniques that can be used in the clinic. His research background is multidisciplinary and spans Bioinformatics, Biomedical Engineering, and Artificial Intelligence applied to healthcare. He authored and co-authored about 48 journal publications (including 6 Nature papers), and over 134 conference posters/papers. He is the chair of the IEEE-BBTC Taskforce of Computational Intelligence in Health and Medicine (CIHM).
Email: ymamatjan@tru.ca
Web: http://www.mamatjanlab.com/
Professor, Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece
Dean of the School of Science, University of Thessaly, Greece
Prof. Vassilis P. Plagianakos is with the Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece. He has served as the President of the Hellenic National Organization for the Provision of Health Services (EOPYY), the Head of the Department of Computer Science and Biomedical Informatics (2014-2019), and the Director of the postgraduate program Informatics and Computational Biomedicine of the School of Sciences (2014-2019). He is also the Founder of the Intelligent Systems Laboratory of the Department of Computer Science and Biomedical Informatics. Prof. Plagianakos is a coauthor of more than 50 journal publications and more than 120 conference papers, and his published work has received more than 4,500 citations. He has served as the Chair of the Board of the Hellenic Artificial Intelligence Society (2017-2019). He is a member of the IEEE Neural Networks Society and the IEEE Bioinformatics and Bioengineering Technical Committee (BBTC). His research interests are in Machine Learning and Neural Networks, Evolutionary and Genetic Algorithms, Intelligent decision-making, Data Mining, Bioinformatics, Clustering, Parallel and Distributed computations, and real-world problem-solving. He co-organized IEEE-BBTC sponsored Hybrid Special Sessions at WCCI 2012, WCCI2014, WCCI 2016, and WCCI 2020.
Email: vpp@uth.gr
Web: https://www.plagianakos.gr/
Project Scientist, Dept. of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles CA
Associate Professor, Electrical Engineering
Missouri Louis Stokes Alliance for Minority Participation (MoLSAMP) MSU site coordinator
Missouri State University
Dr. Tayo Obafemi-Ajayi is the Mace/Turblex associate Professor of Electrical Engineering at Missouri State University in the Cooperative Engineering program (joint agreement with Missouri University of Science and Technology). She is also the faculty director of the Computational Learning Systems lab and the site coordinator for the Missouri Louis Stokes Alliance for Minority Participation (MoLSAMP) at the university. She serves as the chair of IEEE CIS Bioinformatics and Bioengineering Technical Committee (BBTC). She also serves as a Technical Representative on the IEEE Engineering in Medicine and Biology Society (EMBS) Administrative Committee. Her research centers on development and applications of explainable machine learning algorithms. Motivated by practical needs in biomedical applications, her overall goal is to design intelligent systems that analyze large data sets to yield novel discoveries and make meaningful predictions.
Web:
https://www.missouristate.edu/egr/cls/
https://science.missouristate.edu/lsamp
Assistant Professor,
Faculty of Medicine and School of Electrical Engineering & Computer Science
University of Ottawa, Canada
Dr. Mer is an assistant professor at the University of Ottawa, Canada. His primary appointment is at the Faculty of Medicine and is cross-appointed at the School of Electrical Engineering and Computer Science, Faculty of Engineering. His research focuses on developing machine learning methods to analyze large-scale, high-throughput pharmacogenomic datasets to improve personalized medicine. He has authored over 25 peer-reviewed journal publications and 22 conference posters/papers. He has also developed and contributed to multiple bioinformatics software and genomics data analysis projects. He is the vice-chair of the IEEE-BBTC Taskforce of Computational Intelligence in Health and Medicine (CIHM).
Email: amer@uottawa.ca
Professor of Computer Science, Chair of Computational Intelligence, Faculty of Computer Science, Otto von Guericke University Magdeburg
Sanaz Mostaghim is a full professor of computer science, chair of Computational Intelligence, and head of SwarmLab at the Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany. She holds a PhD degree (2004) in electrical engineering from the University of Paderborn, Germany. Sanaz has worked as a postdoctoral fellow at ETH Zurich in Switzerland and as a lecturer at Karlsruhe Institute of Technology (KIT), Germany, where she received her habilitation degree in applied computer science. Her research interests are in the area of multi-criteria optimization and decision-making, evolutionary computation, collective learning and decision-making, and their applications in robotics and science. Sanaz is a member of the Saxon Academy of Sciences, the vice president for member activities of the IEEE Computational Intelligence Society (CIS), IEEE CIS distinguished lecturer (2020-2023), deputy chair of German Informatics, and member of several German and international advisory boards. She is an associate editor of IEEE Transaction on Evolutionary Computation as well as a member of the editorial board of several international journals on AI.
Email : sanaz.mostaghim@ovgu.de
Phone : +49 391 67 54986
Web : https://ci.ovgu.de/Team/Sanaz+Mostaghim.html