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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

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Mar Marcos

    1. Universitat Jaume I, Castellón, Spain

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  2. Jose M. Juarez

    1. University of Murcia, Murcia, Spain

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  3. Richard Lenz

    1. Friedrich-Alexander-University of Erlangen-Nuremberg, Erlangen, Germany

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  4. Grzegorz J. Nalepa

    1. Jagiellonian University and AGH University of Science and Technology, Kraków, Poland

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  5. Slawomir Nowaczyk

    1. Halmstad University, Halmstad, Sweden

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  6. Mor Peleg

    1. University of Haifa, Haifa, Israel

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  7. Jerzy Stefanowski

    1. Poznań University of Technology, Poznan, Poland

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  8. Gregor Stiglic

    1. University of Maribor, Maribor, Slovenia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11979)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (13 papers)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. KR4HC/ProHealth - Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. A Practical Exercise on Re-engineering Clinical Guideline Models Using Different Representation Languages

      • Mar Marcos, Cristina Campos, Begoña Martínez-Salvador
      Pages 3-16

    3. A Method for Goal-Oriented Guideline Modeling in PROforma and Its Preliminary Evaluation

      • Mor Peleg, Alexandra Kogan, Samson W. Tu
      Pages 17-28

    4. Differential Diagnosis of Bacterial and Viral Meningitis Using Dominance-Based Rough Set Approach

      • Ewelina Gowin, Jerzy Błaszczyński, Roman Słowiński, Jacek Wysocki, Danuta Januszkiewicz-Lewandowska
      Pages 29-38

    5. Modelling ICU Patients to Improve Care Requirements and Outcome Prediction of Acute Respiratory Distress Syndrome: A Supervised Learning Approach

      • Mohammed Sayed, David Riaño
      Pages 39-49

    6. Deep Learning for Haemodialysis Time Series Classification

      • Giorgio Leonardi, Stefania Montani, Manuel Striani
      Pages 50-64

  3. TEAAM - Workshop on Transparent, Explainable and Affective AI in Medical Systems

    1. Front Matter

      Pages 65-65
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    2. Towards Understanding ICU Treatments Using Patient Health Trajectories

      • Alexander Galozy, Sławomir Nowaczyk, Anita Sant’Anna
      Pages 67-81

    3. An Explainable Approach of Inferring Potential Medication Effects from Social Media Data

      • Keyuan Jiang, Tingyu Chen, Liyuan Huang, Ravish Gupta, Ricardo A. Calix, Gordon R. Bernard
      Pages 82-92

    4. Exploring Antimicrobial Resistance Prediction Using Post-hoc Interpretable Methods

      • Bernardo Cánovas-Segura, Antonio Morales, Antonio López Martínez-Carrasco, Manuel Campos, Jose M. Juarez, Lucía López Rodríguez et al.
      Pages 93-107

    5. Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening

      • Leon Kopitar, Leona Cilar, Primoz Kocbek, Gregor Stiglic
      Pages 108-119

    6. A Computational Framework Towards Medical Image Explanation

      • Xuwen Wang, Yu Zhang, Zhen Guo, Jiao Li
      Pages 120-131

    7. A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis

      • Erica Ramirez, Markus Wimmer, Martin Atzmueller
      Pages 132-147

    8. Self-organizing Maps Using Acoustic Features for Prediction of State Change in Bipolar Disorder

      • Olga Kamińska, Katarzyna Kaczmarek-Majer, Karol Opara, Wit Jakuczun, Monika Dominiak, Anna Antosik-Wójcińska et al.
      Pages 148-160

    9. Explainable Machine Learning for Modeling of Early Postoperative Mortality in Lung Cancer

      • Katarzyna Kobylińska, Tomasz Mikołajczyk, Mariusz Adamek, Tadeusz Orłowski, Przemysław Biecek
      Pages 161-174

  4. Back Matter

    Pages 175-175
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Other volumes

  1. Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems

Keywords

About this book

This book constitutes revised selected papers from the AIME 2019 workshops KR4HC/ProHealth 2019, the Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, and TEAAM 2019, the Workshop on Transparent, Explainable and Affective AI in Medical Systems.

The volume contains 5 full papers from KR4HC/ProHealth, which were selected out of 13 submissions. For TEAAM 8 papers out of 10 submissions were accepted for publication.

Editors and Affiliations

  • Universitat Jaume I, Castellón, Spain

    Mar Marcos

  • University of Murcia, Murcia, Spain

    Jose M. Juarez

  • Friedrich-Alexander-University of Erlangen-Nuremberg, Erlangen, Germany

    Richard Lenz

  • Jagiellonian University and AGH University of Science and Technology, Kraków, Poland

    Grzegorz J. Nalepa

  • Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  • University of Haifa, Haifa, Israel

    Mor Peleg

  • Poznań University of Technology, Poznan, Poland

    Jerzy Stefanowski

  • University of Maribor, Maribor, Slovenia

    Gregor Stiglic

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