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- | Introduction: 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' | + | {{ :teaam: |
- | 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, | ||
- | * Line regarding interpretability in Machine Learning/AI | ||
- | * Line regarding affective AI in medicine | ||
- | * Data safety - patient data are highly sensitive and require appropriate safety measures and regulation. | ||
- | * Data heterogeneity - medical data comes in many forms including: structured, unstructured, | ||
- | * 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 | ||
- | * Irregularity - due to the heterogeneity of medical conditions, patient-related patterns may be very irregular even for the same disease. | ||
- | Topics of interest: | + | ===== 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\\ | ||
+ | Jerzy Stefanowski, | ||
+ | |||
+ | ===== 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' | ||
+ | |||
+ | ===== 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 | + | * explanatory |
+ | * consequences of black-box AI 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 | + | |
- | * context-aware medical systems | + | ===== Motivation ===== |
- | * empowering patients and self-management | + | 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, |
- | * consequences | + | |
- | * impact | + | |
+ | ===== Schedule ===== | ||
+ | |||
+ | |||
+ | {{: | ||
+ | **Keynote speaker:** Prof. Marcin Grzegorzek, Universität zu Lübeck, Institut für Medizinische Informatik | ||
+ | |||
+ | **Title:** // | ||
+ | |||
+ | **Abstract: | ||
+ | |||
+ | ===== Proceedings ===== | ||
+ | We are aiming at proving CEUR WS proceedings containg all the papers presented at the workshop. Furthermore, | ||
+ | |||
+ | ===== Program Committee ===== | ||
+ | (tentative)\\ | ||
+ | Martin Atzmueller, Univeristy 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\\ | ||
+ | Peter Lucas Leiden University, The Netherlands\\ | ||
+ | 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\\ | ||
+ | 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:// | ||
+ | |||
+ | ===== Schedule ===== | ||
+ | |||
+ | 9:00-10:30 Opening session (Chair: J. Stefanowski) | ||
+ | |||
+ | 9:00 TEAAM Opening: Workshop chairs\\ | ||
+ | 9:15 Marcin Grzegorzek: Human-centred Pattern Recognition for Assistive Health Technologies | ||
+ | |||
+ | 10:30-10:50 Coffee Break | ||
+ | |||
+ | 10:50-11:50 Session 1 (Chair: J. Juarez) | ||
+ | |||
+ | 10:50 Olga Kamińska, et al.: Self organizing maps using acoustic features for prediction of state change in bipolar disorder\\ | ||
+ | 11:10 Alexander Galozy, et al.: Towards Understanding ICU Treatments using Patient Health Trajectories\\ | ||
+ | 11:30 Erica Ramirez, et al.: Interpretable Anomaly Detection | ||
+ | |||
+ | 11:50-12:50 Session 2 (Chair: S. Nowaczyk) | ||
+ | |||
+ | 11:50 Leon Kopitar, et al.: Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening\\ | ||
+ | 12:10 Bernardo Cánovas Segura, et al.: Exploring antimicrobial resistance prediction using post-hoc interpretable methods\\ | ||
+ | 12:30 Katarzyna Kobylińska, | ||
+ | |||
+ | 12:50 - 14:00 Lunch Break | ||
+ | |||
+ | 14:00 - 15:30 Session 3 (Chair: G. Stiglic) | ||
- | Important Dates | + | 14:00 Keyuan Jiang, et al.: An Explainable Approach of Inferring Potential Medication Effects from Social Media Data\\ |
- | * Paper submission: 2019-04-15 | + | 14:20 Xuwen Wang, et al.: A Computational Framework towards Medical Image Explanation |
- | * Notification: 2019-05-13 | + | 14:40 Discussion and workshop closing |
- | * Camera-ready: 2019-06-10 | + | |
- | //More details will follow soon// | + | 15:30 - 16:00 (farewell) Coffee Break |