teaam:start [GEIST Research Group]

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====== 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 [[http://icdm2020.bigke.org/|ICDM 2020: 20th IEEE International Conference on Data Mining]]

Chairs: 
[[https://webs.um.es/jmjuarez|Jose M. Juarez]], 
[[http://gjn.re|Grzegorz J. Nalepa]], 
[[http://islab.hh.se/slanow|Sławomir Nowaczyk]], 
[[http://www.cs.put.poznan.pl/jstefanowski/|Jerzy Stefanowski]],
[[http://www.ri.fzv.um.si/gstiglic/|Gregor Stiglic]].

The 1st edition TEAAM 2019 was held on the [[http://aime19.aimedicine.info|17th Conference on AI in Medicine (AIME)]].
Workshop proceedings [[https://link.springer.com/book/10.1007%2F978-3-030-37446-4]]


===== Organizers =====
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\\

===== Abstract =====
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.


===== Topics of interest ===== 
  * 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

===== Motivation =====  
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.



===== Program Committee ===== 
(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


===== Important Dates =====

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

===== Paper submission =====

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.

===== Schedule =====

TBA