The purpose of XAI-Healthcare event is to provide a place for intensive discussion on all aspects of eXplainable Artificial Intelligence (XAI) in the medical and healthcare field. This should result in cross-fertilization among research on Machine Learning, Decision Support Systems, Natural Language, Human-Computer Interaction, and Healthcare sciences. This meeting will also provide attendees with an opportunity to learn more on the progress of XAI in healthcare and to share their own perspectives. The panel discussion will provide participants with the insights on current developments and challenges from the researchers working in this fast-developing field.
- Primoz Godec, Nikola Dukic, Ajda Pretnar, Vesna Tanko, Lan Zagar and Blaz Zupan. Explainable Point-Based Document Visualizations
- Enea Parimbelli, Giovanna Nicora, Szymon Wilk, Wojtek Michalowski and Riccardo Bellazzi. Tree-based local explanations of machine learning model predictions – AraucanaXAI
- Raphaela Butz, Renee Schulz, Arjen Hommersom and Marko van Eekelen. What is understandable in Bayesian network explanations?
- Pedro Cabalar, Brais Muniz Castro, Gilberto Pérez and Francisco Suarez Lopez. Explanation of decision trees applied to liver transplantation
- Glenn Forbes, Stewart Massie and Susan Craw. Visualisation to Explain Personal Health Trends in Smart Homes
- Chee Keong Wee and Nathan Wee. Association Rules Mining on triaged doctors’ referrals for Otorhinolaryngology
- Chaochen Wu and Guan Luo. Improving Online Health Intent Analysis by Knowledge Inference Encoding
- Edeline Contempre, Zoltan Szlavik, Erick Velazquez Godinez, Annette ten Teije and Ilaria Tiddi. Explainability feature analysis for treatment search engines
We expect the contributions received to describe explanation methods, AI techniques and a targeted healthcare problem. Some examples are provided below for guidance, but the list of topics is not limited to these specific methods, techniques and problems.
- Model agnostic methods
- Feature analysis
- Visualization approaches
- Example-based Explat.
- Fairness, accountability and trust
- Evaluating XAI
- Fairness and bias auditing
- Human-AI interaction
- Human-Computer Interaction (HCI) for XAI
- Blackbox ML approaches: DL, random forest, etc.
- Interpretable ML models: Rules, Trees, etc.
- Statistical methods
- Case-based reasoning
- Natural Language proc. and generation.
Target healthcare problems:
- Infection challenges (COVID, Antibiotic Resistance, etc.)
- Chronic diseases
- Ageing & home care
- April 26, 2021 Paper submission
- May 21, 2021 Acceptance
- May 31, 2021 Final mansucript
- Jun 16, 2021 Workshop
Papers should be submitted to the XAI-Healthcare Easy Chair Website at https://easychair.org/conferences/?conf=xaihealthcare2021 Papers should be formatted according to Springer LNCS format (see http://www.springer.de/comp/lncs/authors.html).
The workshop features regular papers in a single category: short papers (up to 5 pages) describing either original research projects or work-in-progress.
All accepted papers will appear in the conference proceedings which will be published in arXiv after the papers are presented.
Special Issue Journal
Best papers presented will be invited to extend their manuscript for possible publication in the Special Issue on XAI at the Journal of Artificial Intelligence in Medicine (impact factor 4.3).
- Jose M. Juarez, Faculty of Computer Science, University of Murcia [contact]
- Gregor Stiglic, Faculty of Health Sciences, University of Maribor [contact]
- Huang Zhengxing, Faculty of Biomedical Engineering, Zhejiang University[contact]
- Katrien Verbert, Faculty of Engineering Science, KU Leuven[contact]
- Bernardo Canovas, University of Murcia
- Leona Cilar, University of Maribor, Slovenia
- Carlo Combi, University of Verona
- Milos Hauskrecht, University of Pittsburgh
- Zhe He, Florida State University
- Primoz Kocbek, University of Maribor
- Jean-Baptiste Lamy, University Paris 13
- Giorgio Leonardi, Western Piedmont University
- Stewart Massie, Robert Gordon University
- Silvia Miksch, TU Wien
- Denis Parra, PUC
- Alejandro Rodriguez, Tech Univ of Madrid
- Supreeth Shashikumar, UC San Diego
- Simone Stumpf, City University London, United Kingdom
- Buzhou Tang, Harbin Institute of Technology
- Nava Tintarev, Maastricht University, The Netherlands
- Ping Zhang, Ohio State University
Online activities & Program
This workshop will be entirely a life online event (no pre-recorded talks) using ZOOM platform.
XAI-Healthcare includes paper presentations and invited talks related to the workshop topics listed above. All submitted papers have been subject to a review by the workshop Program Committee. Based on the number of high-quality submissions we will define the length of the presentations that will be followed by time for questions and discussion from the audience.
Dr. Marinka Zitnik
Assistan Professor at the Department of Biomedical Informatics, Harvard University.
Associate Member at Broad Institute of MIT and Harvard.
Title: "Actionable Machine Learning for Drug Discovery and Development"+info about M. Zitnik
For attending XAI-Healthcare and the rest of AIME 2021 activities, please visit the AIME2021 website