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ORCIDAIME 2019: Poznan, Poland - KR4HC/ProHealth and TEAAM Workshop
- Mar Marcos
, Jose M. Juarez, Richard Lenz, Grzegorz J. Nalepa, Slawomir Nowaczyk, Mor Peleg, Jerzy Stefanowski, Gregor Stiglic:
Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems - AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26-29, 2019, Revised Selected Papers. Lecture Notes in Computer Science 11979, Springer 2019, ISBN 978-3-030-37445-7
KR4HC/ProHealth - Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care
- Mar Marcos, Cristina Campos, Begoña Martínez-Salvador:
A Practical Exercise on Re-engineering Clinical Guideline Models Using Different Representation Languages. 3-16 - Mor Peleg, Alexandra Kogan, Samson W. Tu:
A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation. 17-28 - Ewelina Gowin, Jerzy Blaszczynski, Roman Slowinski, Jacek Wysocki, Danuta Januszkiewicz-Lewandowska:
Differential Diagnosis of Bacterial and Viral Meningitis Using Dominance-Based Rough Set Approach. 29-38 - Mohammed Sayed, David Riaño:
Modelling ICU Patients to Improve Care Requirements and Outcome Prediction of Acute Respiratory Distress Syndrome: A Supervised Learning Approach. 39-49 - Giorgio Leonardi, Stefania Montani, Manuel Striani:
Deep Learning for Haemodialysis Time Series Classification. 50-64
TEAAM - Workshop on Transparent, Explainable and Affective AI in Medical Systems
- Alexander Galozy, Slawomir Nowaczyk, Anita Pinheiro Sant'Anna:
Towards Understanding ICU Treatments Using Patient Health Trajectories. 67-81 - Keyuan Jiang, Tingyu Chen, Liyuan Huang, Ravish Gupta, Ricardo A. Calix, Gordon R. Bernard:
An Explainable Approach of Inferring Potential Medication Effects from Social Media Data. 82-92 - Bernardo Cánovas-Segura, Antonio Morales Nicolás, Antonio López Martínez-Carrasco, Manuel Campos, Jose M. Juarez, Lucía López-Rodríguez, Francisco Palacios:
Exploring Antimicrobial Resistance Prediction Using Post-hoc Interpretable Methods. 93-107 - Leon Kopitar, Leona Cilar, Primoz Kocbek, Gregor Stiglic:
Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening. 108-119 - Xuwen Wang, Yu Zhang, Zhen Guo, Jiao Li:
A Computational Framework Towards Medical Image Explanation. 120-131 - Erica Ramirez, Markus Wimmer, Martin Atzmueller:
A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis. 132-147 - Olga Kaminska, Katarzyna Kaczmarek-Majer, Karol R. Opara, Wit Jakuczun, Monika Dominiak, Anna Antosik-Wójcinska, Lukasz Swiecicki, Olgierd Hryniewicz:
Self-organizing Maps Using Acoustic Features for Prediction of State Change in Bipolar Disorder. 148-160 - Katarzyna Kobylinska, Tomasz Mikolajczyk, Mariusz Adamek, Tadeusz Orlowski, Przemyslaw Biecek:
Explainable Machine Learning for Modeling of Early Postoperative Mortality in Lung Cancer. 161-174
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