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teaam:start [2019/02/27 08:46] – gjn | teaam:start [2020/04/15 15:52] (current) – [Transparent, Explainable and Affective AI in Medical Systems (TEAAM)] gjn | ||
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- | TEAAM 2019 is a 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]]** |
- | Chairs: [[http:// | ||
- | Introduction: | + | TEAAM 2020 is the 2nd edition |
- | This workshop will bring together researchers from academia and industry to discuss current topics of interest in interpretability, | + | |
- | 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, | + | Chairs: |
- | * Line regarding interpretability in Machine Learning/AI | + | [[https:// |
- | * Line regarding affective AI in medicine | + | [[http://gjn.re|Grzegorz J. Nalepa]], |
- | * Data safety - patient data are highly sensitive and require appropriate safety measures and regulation. | + | [[http://islab.hh.se/ |
- | * Data heterogeneity - medical data comes in many forms including: structured, unstructured, | + | [[http:// |
- | * 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 | + | [[http:// |
- | * Irregularity - due to the heterogeneity of medical conditions, patient-related patterns may be very irregular even for the same disease. | + | |
- | Topics of interest: | + | The 1st edition TEAAM 2019 was held on the [[http:// |
+ | Workshop proceedings [[https:// | ||
+ | |||
+ | |||
+ | ===== Organizers ===== | ||
+ | Jose M. Juarez, University of Murcia, Spain\\ | ||
+ | Grzegorz J. Nalepa, Jagiellonian University, Poland\\ | ||
+ | Sławomir Nowaczyk, Halmstad University, Sweden\\ | ||
+ | Jerzy Stefanowski, | ||
+ | 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' | ||
+ | need for approaches that are comprehensive, | ||
+ | recommendations, | ||
+ | |||
+ | Furthermore, | ||
+ | regulations and the real caution taken by physicians while treating the patients. Improving an | ||
+ | individual' | ||
+ | healthcare team and the patient. Building up this collaboration not only requires individualized | ||
+ | personalisation, | ||
+ | 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, | ||
+ | healthcare domains. | ||
+ | |||
+ | |||
+ | ===== Topics of interest | ||
* explanation in medical systems | * explanation in medical systems | ||
* comprehensive and interpretable knowledge representations | * comprehensive and interpretable knowledge representations | ||
* interpretable machine learning in medical applications | * interpretable machine learning in medical applications | ||
- | * explanation user interfaces and human computer interaction for explainable | + | * human-computer interaction for explainable |
+ | * consequences of black-box intelligent systems in medicine | ||
* ethical aspects, law and social responsibility | * ethical aspects, law and social responsibility | ||
- | | + | * emotion-based personalization |
- | | + | * human-oriented |
- | * affective computing solutions in medicine | + | * patient behaviour change detection |
- | * adaptation in medical systems | + | * context-aware |
- | * patient behaviour change detection | + | * empowering patients and self-management |
- | * person-centered health care | + | * person-centred health care enabled by explainable data mining and machine learning |
- | * context-aware medical systems | + | |
- | * empowering patients and self-management | + | ===== Motivation ===== |
- | * consequences of black-box AI systems | + | 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 | ||
+ | use, the emerging interest in the regulation | ||
+ | modelling becomes increasingly necessary for both data analysts and health care professionals, | ||
+ | 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 | ||
+ | appropriate safety measures and regulation, 4) Data heterogeneity - medical data comes in many forms | ||
+ | including: structured, unstructured, | ||
+ | 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, | ||
+ | Jerzy Błaszczyński, | ||
+ | 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, | ||
+ | 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, | ||
+ | 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:// | ||
+ | We encourage full (12pp) as well as short (6pp) original research papers. | ||
+ | Springer LNCS format of PDF submissions is required. | ||
- | Important Dates | + | ===== Schedule ===== |
- | * Paper submission: | + | |
- | * Notification: | + | |
- | * Camera-ready: | + | |
- | //More details will follow soon// | + | TBA |