IEEE International Workshop on Cyber-Medical Systems
in conjunction with IEEE International Conference on E-health Networking, Application & Services
- Prof. Dr.-Ing. Guido Dartmann, Trier University of Applied Sciences, Germany (firstname.lastname@example.org)
- Prof. Dr.-Ing. Anke Schmeink, RWTH Aachen University, Germany (email@example.com)
- Prof. Dr. Gunes Karabulut Kurt, Istanbul Technical University, Turkey (firstname.lastname@example.org)
- Prof. Dr.-Ing. Gerd Ascheid, RWTH Aachen University, Germany (email@example.com)
- Dr. med. Lukas Martin, RWTH Aachen University, University Hospital, Germany (firstname.lastname@example.org)
- Dr. med. Arne Peine, RWTH Aachen University, University Hospital, Germany (email@example.com)
The objective of the workshop is to establish a forum for the discussion on recent contributions in the field of cyber-medical systems. Progress in medical information and communication technology in combination with advances in machine learning, data analytics and intelligent decisioning allows for the design of completely new data-based medical applications. There is currently great hope in two areas of cyber-medical systems, which we aim at addressing by this workshop:
- Medical-insilico methods for drug optimization: Computer-assisted model-based optimization of drugs (optimization, model checking, etc.).
- Individualized medicine: Automated, computer-assisted determination of therapy decisions. A typical application is for patients who are treated in the intensive care unit, e.g., the control of volume (infusion) therapy, since the amount of infused volume, its composition and the time of substitution significantly influence the morbidity and lethality of the critically ill patient.
This workshop targets these two fields of application and discusses challenges for data analysis and machine learning, the basic methodology for both fields of application. Both applications are characterized by learning with small data sets, expert knowledge and the linking of different models and procedures. From the method perspective, this workshop addresses the following challenges:
- Minimum data sets: Methods for learning with a low amount of training data.
- Contribution of expert knowledge: Methods that include a-priori knowledge of the users, e.g., the medical doctors.
- Concatenation: Methods for the combination of machine learning methods with other methods of artificial intelligence.
However, the methods described above also pose a major threat to data security and privacy. By combining large amounts of data in combination with machine learning, it will be possible to conclude on private information. Furthermore, the collection of this large amount of data also opens up new possibilities for cyber-attacks.
Topics of interest:
The topics of interest include but are not limited to:
- Model checking of medical signals
- Statistical signal processing in medical systems
- Data-bases and ICT infrastructure for cyber-medical systems
- Privacy aspects on data analytics in medical applications
- Security aspects on communication networks in medical systems
- Software and applications on cyber-medical systems
Paper submission deadline: June 15th, 2018
Authors notification: July 10th, 2018
Camera Ready submission: August 1st, 2018