Artificial Intelligence in Medicine

1. Knowledge Representation

   1. Considering Temporal Preferences and Probabilities in Guideline Interaction Analysis

      • Paolo Terenziani, Antonella Andolina

      Pages 120-124

   2. Predicting Patient’s Diagnoses and Diagnostic Categories from Clinical-Events in EHR Data

      • Seyedsalim Malakouti, Milos Hauskrecht

      Pages 125-130

   3. Assessing the Effectiveness of Sequences of Treatments Using Sequential Patterns

      • Maciej Piernik, Joanna Solomiewicz, Arkadiusz Jachnik

      Pages 131-135

2. Probabilistic Models

   1. Front Matter

      Pages 137-137

      PDF

   2. Bayesian Network vs. Cox’s Proportional Hazard Model of PAH Risk: A Comparison

      • Jidapa Kraisangka, Marek J. Druzdzel, Lisa C. Lohmueller, Manreet K. Kanwar, James F. Antaki, Raymond L. Benza

      Pages 139-149

   3. Pursuing Optimal Prediction of Discharge Time in ICUs with Machine Learning Methods

      • David Cuadrado, David Riaño, Josep Gómez, María Bodí, Gonzalo Sirgo, Federico Esteban et al.

      Pages 150-154

   4. Towards the Economic Evaluation of Two Mini-invasive Surgical Techniques for Head&Neck Cancer: A Customizable Model for Different Populations

      • Elisa Salvi, Enea Parimbelli, Lucia Sacchi, Silvana Quaglini, Erika Maggi, Lorry Duchoud et al.

      Pages 155-159

   5. Gated Hidden Markov Models for Early Prediction of Outcome of Internet-Based Cognitive Behavioral Therapy

      • Negar Safinianaini, Henrik Boström, Viktor Kaldo

      Pages 160-169

   6. A Data-Driven Exploration of Hypotheses on Disease Dynamics

      • Marcos L. P. Bueno, Arjen Hommersom, Peter J. F. Lucas, Joost Janzing

      Pages 170-179

   7. On Predicting the Outcomes of Chemotherapy Treatments in Breast Cancer

      • Agastya Silvina, Juliana Bowles, Peter Hall

      Pages 180-190

   8. External Validation of a “Black-Box” Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences?

      • Harry F. da Cruz, Boris Pfahringer, Frederic Schneider, Alexander Meyer, Matthieu-P. Schapranow

      Pages 191-201

3. Behavior Monitoring

   1. Front Matter

      Pages 203-203

      PDF

   2. Classifying Small Volumes of Tissue for Real-Time Monitoring Radiofrequency Ablation

      • Emre Besler, Yearnchee Curtis Wang, Terence Chan, Alan Varteres Sahakian

      Pages 205-215

   3. Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-Based Classification and High-Level Heuristics

      • Patrice C. Roy, William Van Woensel, Andrew Wilcox, Syed Sibte Raza Abidi

      Pages 216-226

   4. NONCADO: A System to Prevent Falls by Encouraging Healthy Habits in Elderly People

      • Elisa Salvi, Silvia Panzarasa, Riccardo Bagarotti, Michela Picardi, Rosangela Boninsegna, Irma Sterpi et al.

      Pages 227-232

   5. The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring

      • Yan Zeng, Paolo Fraccaro, Niels Peek

      Pages 233-238

4. Clustering, Natural Language Processing, and Decision Support

   1. Front Matter

      Pages 239-239

      PDF

   2. AI-Driven Pathology Laboratory Utilization Management via Data- and Knowledge-Based Analytics

      • Syed Sibte Raza Abidi, Jaber Rad, Ashraf Abusharekh, Patrice C. Roy, William Van Woensel, Samina R. Abidi et al.

      Pages 241-251

   3. Using Cluster Ensembles to Identify Psychiatric Patient Subgroups

      • Vincent Menger, Marco Spruit, Wouter van der Klift, Floor Scheepers

      Pages 252-262

   4. Connection Between the Parkinson’s Disease Subtypes and Patients’ Symptoms Progression

      • Anita Valmarska, Dragana Miljkovic, Marko Robnik–Šikonja, Nada Lavrač

      Pages 263-268

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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