Artificial Intelligence in Medicine: 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings | SpringerLink
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Artificial Intelligence in Medicine

21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings

  • Conference proceedings
  • © 2023

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Editors:
  1. Jose M. Juarez

    1. University of Murcia, Murcia, Spain

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  2. Mar Marcos

    1. Universitat Jaume I, Castellón de la Plana, Spain

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  3. Gregor Stiglic

    1. University of Maribor, Maribor, Slovenia

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  4. Allan Tucker

    1. Brunel University London, Uxbridge, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13897)

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

Included in the following conference series:

Conference proceedings info: AIME 2023.

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About this book

This book constitutes the refereed proceedings of the 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, held in Portoroz, Slovenia, in June12–15, 2023.

The 23 full papers and 21 short papers presented together with 3 demonstration papers were selected from 108 submissions. The papers are grouped in topical sections on: machine learning and deep learning; explainability and transfer learning; natural language processing; image analysis and signal analysis; data analysis and statistical models; knowledge representation and decision support.

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Table of contents (47 papers)

  1. Front Matter

    Pages i-xviii
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  2. Machine Learning and Deep Learning

    1. Front Matter

      Pages 1-1
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    2. Survival Hierarchical Agglomerative Clustering: A Semi-Supervised Clustering Method Incorporating Survival Data

      • Alexander Lacki, Antonio Martinez-Millana
      Pages 3-12

    3. Boosted Random Forests for Predicting Treatment Failure of Chemotherapy Regimens

      • Muhammad Usamah Shahid, Muddassar Farooq
      Pages 13-24

    4. A Binning Approach for Predicting Long-Term Prognosis in Multiple Sclerosis

      • Robbe D’hondt, Sinéad Moylett, An Goris, Celine Vens
      Pages 25-34

    5. Decision Tree Approaches to Select High Risk Patients for Lung Cancer Screening Based on the UK Primary Care Data

      • Teena Rai, Yuan Shen, Jaspreet Kaur, Jun He, Mufti Mahmud, David J. Brown et al.
      Pages 35-39

    6. Causal Discovery with Missing Data in a Multicentric Clinical Study

      • Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas, Hanny Pijnenborg, Casper Reijnen, Marco Scutari et al.
      Pages 40-44

    7. Novel Approach for Phenotyping Based on Diverse Top-K Subgroup Lists

      • Antonio Lopez-Martinez-Carrasco, Hugo M. Proença, Jose M. Juarez, Matthijs van Leeuwen, Manuel Campos
      Pages 45-50

    8. Patient Event Sequences for Predicting Hospitalization Length of Stay

      • Emil Riis Hansen, Thomas Dyhre Nielsen, Thomas Mulvad, Mads Nibe Strausholm, Tomer Sagi, Katja Hose
      Pages 51-56

    9. Autoencoder-Based Prediction of ICU Clinical Codes

      • Tsvetan R. Yordanov, Ameen Abu-Hanna, Anita CJ. Ravelli, Iacopo Vagliano
      Pages 57-62

  3. Explainability and Transfer Learning

    1. Front Matter

      Pages 63-63
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    2. Hospital Length of Stay Prediction Based on Multi-modal Data Towards Trustworthy Human-AI Collaboration in Radiomics

      • Hubert Baniecki, Bartlomiej Sobieski, Przemysław Bombiński, Patryk Szatkowski, Przemysław Biecek
      Pages 65-74

    3. Explainable Artificial Intelligence for Cytological Image Analysis

      • Stefan Röhrl, Hendrik Maier, Manuel Lengl, Christian Klenk, Dominik Heim, Martin Knopp et al.
      Pages 75-85

    4. Federated Learning to Improve Counterfactual Explanations for Sepsis Treatment Prediction

      • Christoph Düsing, Philipp Cimiano
      Pages 86-96

    5. Explainable AI for Medical Event Prediction for Heart Failure Patients

      • Weronika Wrazen, Kordian Gontarska, Felix Grzelka, Andreas Polze
      Pages 97-107

    6. Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection

      • João Vitorino, Lourenço Rodrigues, Eva Maia, Isabel Praça, André Lourenço
      Pages 108-113

    7. Computational Evaluation of Model-Agnostic Explainable AI Using Local Feature Importance in Healthcare

      • Seda Polat Erdeniz, Michael Schrempf, Diether Kramer, Peter P. Rainer, Alexander Felfernig, Trang Tran et al.
      Pages 114-119

    8. Batch Integrated Gradients: Explanations for Temporal Electronic Health Records

      • Jamie Duell, Xiuyi Fan, Hsuan Fu, Monika Seisenberger
      Pages 120-124

    9. Improving Stroke Trace Classification Explainability Through Counterexamples

      • Giorgio Leonardi, Stefania Montani, Manuel Striani
      Pages 125-129

    10. Spatial Knowledge Transfer with Deep Adaptation Network for Predicting Hospital Readmission

      • Ameen Abdel Hai, Mark G. Weiner, Alice Livshits, Jeremiah R. Brown, Anuradha Paranjape, Zoran Obradovic et al.
      Pages 130-139
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Other volumes

  1. Artificial Intelligence in Medicine

Editors and Affiliations

  • University of Murcia, Murcia, Spain

    Jose M. Juarez

  • Universitat Jaume I, Castellón de la Plana, Spain

    Mar Marcos

  • University of Maribor, Maribor, Slovenia

    Gregor Stiglic

  • Brunel University London, Uxbridge, UK

    Allan Tucker

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