The special track on “Artificial Intelligence for Healthcare: from black box to explainable models” - AI4H:B2E 2019 - aims at bringing together researchers from academia, industry, government and medical centers in order to present the state of the art and discuss the latest advances in the emerging area of the use of Artificial Intelligence (AI) and Soft Computing (SC) techniques in the fields of medicine, biology, healthcare and wellbeing.
In general, in recent years, methods based on AI and SC have proved to be extremely useful in a wide variety of areas, and are becoming more and more widespread, in some cases a sort of a “de facto” standard.
Currently, many of the algorithms on offer are often black box in nature (defined as a system which can be viewed in terms of its inputs and outputs without any knowledge of its internal workings). This may not be an issue for certain practical AI solutions in healthcare, yet in other systems it may indeed be a serious limitation. This holds true when a clear explanation should be provided to a user about the reasons why a solution is proposed by an AI-based system. In fact, if the predictive models are not transparent and explainable, we lose the trust of experts such as healthcare practitioners. Moreover, without access to the knowledge of how an algorithm works we cannot truly understand the underlying meaning of the output.
Given the above general framework, AI4H:B2E is expected to cover the whole range of methodological and practical aspects related to the use of AI and SC in Healthcare:
- we request papers that explore methods to combine state-of-the-art data analytics for exploiting the huge data resources available, while ensuring that these systems are explainable to domain experts. This will result in systems that not only generate new insights but are also more fully trusted.
- we also request papers that describe more generally the successful application of AI and SC methodologies to issues as machine learning, deep learning, knowledge discovery, decision support, regression, forecasting, optimization and feature selection in the healthcare, biology, medicine and wellbeing domains..
The topics of interest include, but are not limited to:
- explainable AI models:
- Rule and Logic Based Explanation;
- Deep Learning and methods to explain Hidden Layers;
- Assistive Technology (AT);
- Recommender Systems;
- Natural Language for Explanation;
- Visualisation & Interactive Interfaces;
- the general application of AI and SC methodologies, in Health, Biology and Medicine to issues such as:
- Knowledge Management of Health Data;
- Data Mining and Knowledge Discovery in Healthcare;
- Machine and Deep learning approaches for Health Data;
- Decision Support Systems for Healthcare and Wellbeing;
- Optimization for Healthcare problems;
- Regression and Forecasting for medical and/or biomedical signals;
- Healthcare Information Systems;
- Wellness Information Systems;
- Medical Signal and Image Processing and Techniques;
- Medical Expert Systems;
- Diagnosis and Therapy Support Systems;
- Biomedical Applications;
- Applications of AI in Healthcare and Wellbeing Systems;
- Machine Learning-based Medical Systems;
- Medical Data and Knowledge Bases;
- Neural Networks in Medicine;
- Ambient Intelligence and Pervasive Computing in Medicine and Healthcare.
TCP to be expanded. Should you wish to join it, please contact us
- Mahir Arzoky, Brunel University - United Kingdom
- Marco Avvenuti, University of Pisa - Italy
- Grigorios Beligiannis, University of Patras - Greece
- Susana Brás, Universidade de Aveiro - Portugal
- Stefano Cagnoni, University of Parma - Italy
- Lorenzo Carnevale, University of Messina - Italy
- Fabrizio Celesti, University of Messina - Italy
- Christos Chrysoulas, London South Bank University - United Kingdom
- Miguel Coimbra, University of Porto - Portugal
- Vasa Curcin, King's College London - United Kingdom
- Arianna Dagliati, University of Manchester - United Kingdom
- Antonio Della Cioppa, University of Salerno - Italy
- Maria Fazio, University of Messina - Italy
- Stefka Fidanova, Institute of Information and Communication Technologies - Bulgaria
- Elisa Fromont, Université de Rennes 1 - France
- Sebastian Fudickar, University of Oldenburg - Germany
- Antonino Galletta, University of Messina - Italy
- Jaakko Hollmén, Aalto University - Finland
- John Holmes, University of Pennsylvania - USA
- Tomas Koutny, University of West Bohemia - Czech Republic
- Xing Liu, Kwantlen Polytechnic University - Canada
- Xiaohui Liu, Brunel University - United Kingdom
- Simone Marini, University of Pavia - Italy
- Panagiotis Papapetrou, Stockholm University - Sweden
- Simon Parsons, King's College London - United Kingdom
- Dipti Patil, Pune University - India
- Niels Peek, The University of Manchester - United Kingdom
- Pedro Pereira Rodrigues, University of Porto - Portugal
- Seyedamin Pouriyeh, Kennesaw State University - USA
- Lucia Sacchi, University of Pavia - Italy
- Marco Scutari, University of Oxford - United Kingdom
- Ovidiu Serban, Imperial College London - United Kingdom
- Stefano Silvestri, Institute for High Performance Computing and Networking, ICAR-CNR - Italy
- Jan Sliwa, University of Applied Sciences Bern - Switzerland
- Francesca Toni, Imperial College London - United Kingdom
- Lucia Vaira, University of Salento - Italy
- Laura Verde, University of Naples Parthenope - Italy
- Natalia Viani, University of Pavia - Italy
- Matthew Williams, Imperial College London - United KIngdom
- David Wong, University of Leeds - United Kingdom
- Matthijs van Leeuwen, Leiden University - The Netherlands
- Marco Zappatore, University of Salento - Italy
A "Best Paper Award" will be conferred on the author(s) of a paper presented at the Special Track, selected by the Chairs based on the best combined marks of paper reviewing, assessed by the Program Committee. This best paper award is technically sponsored by the Institute of High Performance and Computing of the National Research Council of Italy (ICAR- CNR).
Late registration: May 6, 2019
