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teaam:start [2019/03/01 09:23] – first cfp gjnteaam:start [2020/04/15 15:52] (current) – [Transparent, Explainable and Affective AI in Medical Systems (TEAAM)] gjn
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 ====== Transparent, Explainable and Affective AI in Medical Systems (TEAAM) ====== ====== Transparent, Explainable and Affective AI in Medical Systems (TEAAM) ======
  
-TEAAM 2019 is workshop to be held on the [[http://aime19.aimedicine.info|17th Conference on AI in Medicine (AIME)]]+**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]]
  
-Chairs: [[http://gjn.re|Grzegorz J. Nalepa]], [[http://www.ri.fzv.um.si/gstiglic/|Gregor Stiglic]], [[http://islab.hh.se/slanow|Sławomir Nowaczyk]], [[https://webs.um.es/jmjuarez|Jose M. Juarez]], [[http://www.cs.put.poznan.pl/jstefanowski/|Jerzy Stefanowski]] 
  
 ===== Organizers ===== ===== Organizers =====
-Grzegorz J. Nalepa, AGH University of Science and Technology, Jagiellonian University, Poland\\ 
-Gregor Stiglic, University of Maribor, Slovenia\\ 
-Sławomir Nowaczyk, Halmstad University, Sweden\\ 
 Jose M. Juarez, University of Murcia, Spain\\ Jose M. Juarez, University of Murcia, Spain\\
-Jerzy Stefanowski, Poznan University of Technology, Poland+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 ===== ===== Abstract =====
-Medical systems highlight important requirements and challenges for the AI solutions. In particular, demands for interpretability of models and knowledge representations are much higher than in other domains. The current health-related AI applications rarely provide an integrated yet transparent and humanized solutions. However, from both patient's and doctor's perspective, there is need for approaches that are comprehensive, credible and trusted. By explaining the reasoning behind recommendations, the medical AI systems support users to accept or reject their predictions. Furthermore, healthcare is particularly challenging due to medicine and ethical requirements, laws and regulations and the real caution taken by physicians while treating the patients. Improving individual's health is a complex process, requiring understanding and collaboration between the doctor and the patient. Building up this collaboration not only requires individualized personalization, 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 next 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 AI based systems present in different healthcare domains.+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 
 +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 =====  ===== Topics of interest ===== 
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   * comprehensive and interpretable knowledge representations   * comprehensive and interpretable knowledge representations
   * interpretable machine learning in medical applications   * interpretable machine learning in medical applications
-  * explanatory user interfaces and human computer interaction for explainable AI+  * human-computer interaction for explainable machine learning and pattern recognition 
 +  * consequences of black-box intelligent systems in medicine
   * ethical aspects, law and social responsibility   * ethical aspects, law and social responsibility
   * emotion-based personalization and affective computing solutions in medicine   * emotion-based personalization and affective computing solutions in medicine
   * human-oriented adaptation in medical systems   * human-oriented adaptation in medical systems
-  * patient behaviour change detection +  * patient behaviour change detection and explanation transparency in person-centered health care 
-  * person-centered health care +  * context-aware interpretable medical systems 
-  * context-aware medical systems +  * empowering patients and self-management through understandable AI 
-  * empowering patients and self-management +  * person-centred health care enabled by explainable data mining and machine learning
-  * consequences of black-box AI systems in medicine+
  
 ===== Motivation =====   ===== Motivation =====  
-The investment and development of AI 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 of explanations. Predictive modeling becomes increasingly necessary for both data analysts and healthcare professionals, as it offers unique opportunities for deriving healthcare 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 as1) Line regarding interpretability in Machine Learning/AI, 2) Line regarding affective AI in medicine, 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 maybe 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.+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.
  
-===== Format =====   
-The proposed workshop will include paper presentations and invited talks related to the workshop topics listed above, as well as a panel discussion. All submitted papers will be subject to a review by the workshop Program Committee . Based on the number of high quality submissions we will define the length of the presentations that will be followed by time for questions and discussion from the audience.  
  
-===== Proceedings =====  
-We are aiming at proving CEUR WS proceedings containg all the papers presented at the workshop. Furthermore, we are considering a proposal of a special issue of a JCR journal. 
  
 ===== Program Committee =====  ===== Program Committee ===== 
-(tentative)+(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
  
-Martin Atzmueller, Univeristy of Tilburg, The Netherlands 
  
-Piotr Augustyniak, AGH University of Science and Technology, Poland+===== Important Dates =====
  
-Hendrik Blockeel, Katholieke Universiteit Leuven, Belgium+  * Paper submission:  
 +  * Notification: 
 +  * Camera-ready: 
 +  * Workshop:
  
-Jerzy Błaszczyński, Poznań University of Technology, Poland+===== Paper submission =====
  
-David Camacho, Universidad Autonoma de Madrid, Spain+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.
  
-Manuel Campos, University of Murcia, Spain +===== Schedule =====
- +
-Alex Freitas, University of Kent, United Kingdom +
- +
-Johannes Fürnkranz. TU Darmstadt, Germany +
- +
-Marcin Grzegorzek, Universität zu Lübeck, Germany +
- +
-Giorgio Leonardi, University Piemonte Orientale, Italy +
- +
-Peter Lucas Leiden University, The Netherlands +
- +
-Agnieszka Ławrynowicz, Poznań University of Technology, Poland +
- +
-Erini Ntoutsi, Leibniz University Hannover, Germany +
- +
-Jose Palma, University of Murcia, Spain +
- +
-John F. Rauthmann, Universität zu Lübeck, Germany +
- +
-Spiliopoulou Otto-von-Guericke-University Magdeburg, Germany +
- +
-===== Important Dates ===== +
- +
-  * Paper submission: 2019-04-15 +
-  * Notification: 2019-05-13 +
-  * Camera-ready: 2019-06-10 +
-  * Workshop: TBD +
- +
-===== Paper submission =====+
  
-A separate Easychair installation will be provided. Springer LNCS format of PDF submissions is required.+TBA
  
teaam/start.txt · Last modified: 2020/04/15 15:52 by gjn
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