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ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics

Marcin Kapcia, Hassan Eshkiki, Jamie Duell, Xiuyi Fan, Shangming Zhou, Benjamin Mora Orcid Logo

2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), Pages: 841 - 845

Swansea University Authors: Hassan Eshkiki, Xiuyi Fan, Benjamin Mora Orcid Logo

Abstract

The recent explosion of demand for Explainable AI (XAI) techniques has encouraged the development of various algorithms such as the Local Interpretable Model-Agnostic Explanations (LIME) and the SHapley Additive exPlanations ones (SHAP). Although these algorithms have been widely discussedby the AI...

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Published in: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
ISBN: 978-1-6654-0899-8 978-1-6654-0898-1
ISSN: 1082-3409 2375-0197
Published: IEEE 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58534
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Abstract: The recent explosion of demand for Explainable AI (XAI) techniques has encouraged the development of various algorithms such as the Local Interpretable Model-Agnostic Explanations (LIME) and the SHapley Additive exPlanations ones (SHAP). Although these algorithms have been widely discussedby the AI community, their applications to wider domains are rare, potentially due to the lack of easy-to-use tools built around these methods. In this paper, we present ExMed, a tool that enables XAI data analytics for domain experts without requiring explicit programming skills. In particular, it supports data analytics with multiple feature attribution algorithms for explaining machine learning classifications and regressions. We illustrate its domain of applications on two real world medicalcase studies, with the first one analysing COVID-19 control measure effectiveness and the second one estimating lung cancer patient life expectancy from the artificial Simulacrum health dataset. We conclude that ExMed can provide researchers and domain experts with a tool that both concatenates flexibility and transferability of medical sub-domains and reveal deep insights from data.
College: College of Science
Start Page: 841
End Page: 845