Transparent, Explainable and Affective AI in Medical Systems (TEAAM)

The permanent webpage for TEAAM is http://teaam.geist.re

TEAAM 2020 is the 2nd edition of workshop to be held on the ICDM 2020: 20th IEEE International Conference on Data Mining

Chairs: Jose M. Juarez, Grzegorz J. Nalepa, Sławomir Nowaczyk, Jerzy Stefanowski, Gregor Stiglic.

The 1st edition TEAAM 2019 was held on the 17th Conference on AI in Medicine (AIME). Workshop proceedings https://link.springer.com/book/10.1007%2F978-3-030-37446-4

Jose M. Juarez, University of Murcia, Spain
Grzegorz J. Nalepa, Jagiellonian University, Poland
Sławomir Nowaczyk, Halmstad University, Sweden
Jerzy Stefanowski, Poznan University of Technology, Poland
Gregor Stiglic, University of Maribor, Slovenia

Medical systems highlight important requirements and challenges for AI and data mining solutions. In particular, demands for interpretability of models and knowledge representations are much higher than in other domains. The current health-related AI/ML/DM applications rarely provide integrated yet transparent and humanized solutions. However, from both patient's and doctor's perspective, there is a need for approaches that are comprehensive, credible and trusted. By explaining the reasoning behind recommendations, the medical intelligent systems support users to accept or reject their predictions.

Furthermore, healthcare is particularly challenging due to medical and ethical requirements, laws and regulations and the real caution taken by physicians while treating the patients. Improving an individual's health is a complex process, requiring understanding and collaboration between the healthcare team and the patient. Building up this collaboration not only requires individualized personalisation, but also a proper adaptation to the gradual changes of patient’s condition, including their emotional state. Recently, AI solutions have been playing an important mediating role in understanding how both medical and personal factors interact with respect to diagnosis and treatment adherence. As the number of such applications is expected to rapidly grow in the next few years, their humanized aspect will play a critical role in their adoption.

This workshop will bring together researchers from academia and industry to discuss current topics of interest in interpretability, explainability and affect related to intelligent systems present in different healthcare domains.

• explanation in medical systems
• comprehensive and interpretable knowledge representations
• interpretable machine learning in medical applications
• human-computer interaction for explainable machine learning and pattern recognition
• consequences of black-box intelligent systems in medicine
• ethical aspects, law and social responsibility
• emotion-based personalization and affective computing solutions in medicine
• human-oriented adaptation in medical systems
• patient behaviour change detection and explanation transparency in person-centered health care
• context-aware interpretable medical systems
• empowering patients and self-management through understandable AI
• person-centred health care enabled by explainable data mining and machine learning

The investment and development of AI, machine learning and data mining in the clinical field offers huge societal benefits in the current era of digital medicine, with a significant amount of data around healthcare processes captured in the form of Electronic Health Records, health insurance claims, medical imaging databases, disease registries, spontaneous reporting sites, clinical trials, etc. This positive impact is put under the spotlight regarding the medical responsibilities, the potentially harmful use, the emerging interest in the regulation of algorithms and the need for explanations. Predictive modelling becomes increasingly necessary for both data analysts and health care professionals, as it offers unique opportunities for deriving health care insights. At the same time, these opportunities come with significant dangers and risks that are unlike anything we have seen in the past. This controversial discussion provides a number of research challenges such as 1) interpretability in Machine Learning/AI, 2) affective AI in medicine and healthcare, 3) Data safety - patient data are highly sensitive and require appropriate safety measures and regulation, 4) Data heterogeneity - medical data comes in many forms including: structured, unstructured, text, images, continuous signals from sensors, etc., 5) Sparsity, imperfectness and data gaps – patient records may be sparse due to infrequent clinical visits, and often, data are not equally collected at each medical encounter as well as they are affected by various sources of imperfectness.

(tentative)
Martin Atzmueller, University of Tilburg, The Netherlands
Piotr Augustyniak, AGH University of Science and Technology, Poland
Jerzy Błaszczyński, Poznań Supercomputing and Networking Center, Poland
David Camacho, Universidad Autonoma de Madrid, Spain
Manuel Campos, University of Murcia, Spain
Alex Freitas, University of Kent, United Kingdom
Alejandro Rodríguez González, Universidad Politecnica de Madrid
Marcin Grzegorzek, Universität zu Lübeck, Germany
Jean-Baptiste Lamy, University Paris 13, France
Giorgio Leonardi, University Piemonte Orientale, Italy
Helena Lindgren, Umeå University, Sweden
Zachary Lipton, Carnegie Mellon University, USA
Agnieszka Ławrynowicz, Poznań University of Technology, Poland
Juan Carlos Nieves, Umeå University, Sweden
Erini Ntoutsi, Leibniz University Hannover, Germany
Jose Palma, University of Murcia, Spain
Niels Peek, University of Manchester, United Kingdom
Petra Povalej Brzan, University of Maribor, Slovenia
John F. Rauthmann, Universität zu Lübeck, Germany
Myra Spiliopoulou, Otto-von-Guericke-University Magdeburg, Germany
Stephen Swift, Brunel University, United Kingdom
Szymon Wilk, Poznań University of Technology, Poland
Allan Tucker, Brunel University, United Kingdom
Cristina Soguero Ruiz, Universidad Rey Juan Carlos, Spain

• Paper submission:
• Notification:
• Camera-ready:
• Workshop:

The Easychair installation at https://easychair.org/conferences/?conf=teaam2020 should be used for submissions. We encourage full (12pp) as well as short (6pp) original research papers. Springer LNCS format of PDF submissions is required.

TBA