Artificial Intelligence in Medicine

1. Front Matter

   Pages i-xviii

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2. Invited Speakers

   1. Front Matter

      Pages 1-1

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   2. Common Misconceptions and Future Directions for AI in Medicine: A Physician-Data Scientist Perspective

      • Anthony Chang

      Pages 3-6

   3. AI in Healthcare: Ethical and Privacy Challenges

      • Ivana Bartoletti

      Pages 7-10

3. Deep Learning

   1. Front Matter

      Pages 11-11

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   2. Recent Context-Aware LSTM for Clinical Event Time-Series Prediction

      • Jeong Min Lee, Milos Hauskrecht

      Pages 13-23

   3. Deep Learning Approach for Pathogen Detection Through Shotgun Metagenomics Sequence Classification

      • Ying-Feng Hsu, Makiko Ito, Takumi Maruyama, Morito Matsuoka, Nicolas Jung, Yuki Matsumoto et al.

      Pages 24-30

   4. Fully Interactive Lungs Auscultation with AI Enabled Digital Stethoscope

      • Tomasz Grzywalski, Riccardo Belluzzo, Mateusz Piecuch, Marcin Szajek, Anna Bręborowicz, Anna Pastusiak et al.

      Pages 31-35

   5. An Automated Fall Detection System Using Recurrent Neural Networks

      • Francisco Luna-Perejon, Javier Civit-Masot, Isabel Amaya-Rodriguez, Lourdes Duran-Lopez, Juan Pedro Dominguez-Morales, Anton Civit-Balcells et al.

      Pages 36-41

   6. A Semi-supervised Learning Approach for Pan-Cancer Somatic Genomic Variant Classification

      • Giovanna Nicora, Simone Marini, Ivan Limongelli, Ettore Rizzo, Stefano Montoli, Francesca Floriana Tricomi et al.

      Pages 42-46

   7. ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification

      • Subhrajit Roy, Isabell Kiral-Kornek, Stefan Harrer

      Pages 47-56

   8. Convolutional Recurrent Neural Network for Bubble Detection in a Portable Continuous Bladder Irrigation Monitor

      • Xiaoying Tan, Gerd Reis, Didier Stricker

      Pages 57-66

   9. Mammogram Classification with Ordered Loss

      • Rami Ben-Ari, Yoel Shoshan, Tal Tlusty

      Pages 67-76

4. Simulation

   1. Front Matter

      Pages 77-77

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   2. Agent-Based Models and Spatial Enablement: A Simulation Tool to Improve Health and Wellbeing in Big Cities

      • Daniele Pala, John Holmes, José Pagàn, Enea Parimbelli, Marica Teresa Rocca, Vittorio Casella et al.

      Pages 79-83

   3. Towards Health 4.0: e-Hospital Proposal Based Industry 4.0 and Artificial Intelligence Concepts

      • Camilo Cáceres, Joao Mauricio Rosário, Dario Amaya

      Pages 84-89

5. Knowledge Representation

   1. Front Matter

      Pages 91-91

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   2. MitPlan: A Planning Approach to Mitigating Concurrently Applied Clinical Practice Guidelines

      • Martin Michalowski, Szymon Wilk, Wojtek Michalowski, Marc Carrier

      Pages 93-103

   3. Automatic Alignment of Surgical Videos Using Kinematic Data

      • Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, François Petitjean, Lhassane Idoumghar, Pierre-Alain Muller

      Pages 104-113

   4. A Rule-Based Expert System for Automatic Implementation of Somatic Variant Clinical Interpretation Guidelines

      • Giovanna Nicora, Ivan Limongelli, Riccardo Cova, Matteo Giovanni Della Porta, Luca Malcovati, Mario Cazzola et al.

      Pages 114-119

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019.
The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

• modeling and simulation
• knowledge representation and reasoning
• modeling methodologies
• cluster analysis
• natural language processing
• model development and analysis
• feature selection
• computer vision
• machine learning
• unsupervised learning
• artificial intelligence
• classification
• clustering
• decision trees
• learning algorithms
• machine learning
• neural networks
• semantics
• semantics

• Universitat Rovira i Virgili, Tarragona, Spain

  David Riaño

• Poznan University of Technology, Poznan, Poland

  Szymon Wilk

• VU Amsterdam, Amsterdam, The Netherlands

  Annette ten Teije

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