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--- teaam:start [2019/03/01 09:23] – first cfp gjn
+++ teaam:start [2019/03/04 21:06] – [Program Committee] gjn
@@ Line 4: / Line 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]]
+{{ :teaam:aime2019teaam-cfp2.pdf |Call for papers}}
 ===== Organizers =====
@@ Line 20: / Line 23: @@
   * interpretable machine learning in medical applications
   * explanatory user interfaces and human computer interaction for explainable AI
+  * consequences of black-box AI 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
+  * 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
-  * consequences of black-box AI systems in medicine
 ===== 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 as: 1) 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 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 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, 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.
 ===== Format =====
@@ Line 40: / Line 43: @@
 ===== Program Committee =====
 (tentative)
+Martin Atzmueller, Univeristy of Tilburg, The Netherlands\\
-Martin Atzmueller, Univeristy of Tilburg, The Netherlands
+Piotr Augustyniak, AGH University of Science and Technology, Poland\\
+Jerzy Błaszczyński, Poznań University of Technology, Poland\\
-Piotr Augustyniak, AGH University of Science and Technology, Poland
+David Camacho, Universidad Autonoma de Madrid, Spain\\
+Manuel Campos, University of Murcia, Spain\\
-Hendrik Blockeel, Katholieke Universiteit Leuven, Belgium
+Alex Freitas, University of Kent, United Kingdom\\
+Marcin Grzegorzek, Universität zu Lübeck, Germany\\
-Jerzy Błaszczyński, Poznań University of Technology, Poland
+Jean-Baptiste Lamy,  University Paris 13, France\\
+Giorgio Leonardi, University Piemonte Orientale, Italy\\
-David Camacho, Universidad Autonoma de Madrid, Spain
+Helena Lindgren, Umeå University, Sweden\\
+Zachary Lipton, Carnegie Mellon University, USA\\
-Manuel Campos, University of Murcia, Spain
+Peter Lucas Leiden University, The Netherlands\\
+Agnieszka Ławrynowicz, Poznań University of Technology, Poland\\
-Alex Freitas, University of Kent, United Kingdom
+Juan Carlos Nieves, Umeå University, Sweden\\
+Erini Ntoutsi, Leibniz University Hannover, Germany\\
-Johannes Fürnkranz. TU Darmstadt, Germany
+Jose Palma, University of Murcia, Spain\\
+Niels Peek, University of Manchester, United Kingdom\\
-Marcin Grzegorzek, Universität zu Lübeck, Germany
+Petra Povalej Brzan, University of Maribor, Slovenia\\
+John F. Rauthmann, Universität zu Lübeck, Germany\\
-Giorgio Leonardi, University Piemonte Orientale, Italy
+Myra Spiliopoulou, Otto-von-Guericke-University Magdeburg, Germany\\
+Stephen Swift, Brunel University, United Kingdom\\
-Peter Lucas Leiden University, The Netherlands
+Allan Tucker, Brunel University, United Kingdom\\
+Cristina Soguero Ruiz, Universidad Rey Juan Carlos, Spain\\
-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 =====
@@ Line 78: / Line 72: @@
   * Notification:		2019-05-13
   * Camera-ready:	2019-06-10
-  * Workshop:		TBD
+  * Workshop:		TBD, during AIME 2019-06-26-29
 ===== Paper submission =====
-A separate Easychair installation will be provided. Springer LNCS format of PDF submissions is required.
+The Easychair installation at https://easychair.org/conferences/?conf=teaam2019 shoud be used for submissions. We encourage full (12pp) as well as short (6pp) original research papers. Springer LNCS format of PDF submissions is required.