Artificial Intelligence for Healthcare: from black box to explainable models

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 mode 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.
• 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.

• Prospective authors are invited to submit papers in any of the topics listed above.
• Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
• Please also check the Guidelines.
• Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.

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 (ICARCNR).

• Elizabeth Black (King’s College, UK)
• Ivanoe De Falco (CNR – ICAR, Italy)
• Francesco Gargiulo (CNR – ICAR, Italy)
• Alina Miron (Brunel, UK)
• Giovanna Sannino (CNR – ICAR, Italy)
• Stephen Swift (Brunel, UK)
• Allan Tucker (Brunel, UK)
• Giuseppe De Pietro (CNR-ICAR, Italy)

• Antoine Bagula, University of the Western Cape, South Africa
• Nizar Bouguila, Concordia, Canada
• Giuseppe Caggianese, ICAR-CNR, Italy
• Antonio Celesti, University of Messina, Italy
• Mario Ciampi, ICAR-CNR, Italy
• Carlos Cotta, Universidad de Malaga, Spain
• Arianna Dagliati, Manchester University, UK
• Stefka Fidanova, Bulgarian Academy of Sciences, Bulgaria
• Sebastian Fudickar, University of Oldenburg, Germany
• Antonino Galletta, University of Messina, Italy
• David Garway Heath, Moorfields Eye Hospital, UK
• Mario Giacobini, University of Torino, Italy
• Saemundur Haraldsson, University of Stirling, United Kingdom
• Jaakko Hollmen, Aalto University, Helsinki, Finland
• Nantia Iakovidou, Aristotle University, Greece
• Mario Köppen, Kyushu Institute of Technology, Japan
• Tomáš Koutny, University of West Bohemia, Czech Republic
• Simone Marini, University of Michigan, USA
• Jolanta Mizera-Pietraszko, Wroclaw University of Technology, Poland
• Pooya Moradian Zadeh, University of Windsor, Canada
• Panagiotis Papapetrou, Stockholm, Sweden
• Stelios Pavlidis, Imperial College London, UK
• Niels Peek, Manchester University, UK
• André Pinho, University of Beira Interior, Portugal
• Nuno Pombo, University of Beira Interior, Portugal
• Seyedamin Pouriyeh, University of Georgia, United States
• Pedro Rodrigues, Porto, Portugal
• Alejandro Rodríguez González, Madrid, Spain
• José Santamaría López, University of Jaen, Spain
• Stefano Silvestri, ICAR-CNR, Italy
• Jan Sliwa, Bern University of Applied Sciences, Switzerland
• Berglind Smaradottir, University of Agder, Norway
• Myra Spiliopoulou, Magdeburg, Germany
• Anastasia Theodouli, Aristotle University of Thessaloniki, Greece
• Laura Verde, University of Naples Parthenope, Italy
• Francesca Vitali, University of Arizona, Tucson, USA
• Shuang Wang, University of California San Diego, United States