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Conference Paper/Proceeding/Abstract 902 views 639 downloads

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

Marcin Kapcia, Hassan Eshkiki Orcid Logo, 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 Orcid Logo, Xiuyi Fan, Benjamin Mora Orcid Logo

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DOI (Published version): 10.1109/ictai52525.2021.00134

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
first_indexed 2021-11-02T16:18:11Z
last_indexed 2022-01-13T04:28:25Z
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recordtype SURis
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spelling 2022-01-12T17:04:22.8138740 v2 58534 2021-11-02 ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics c9972b26a83de11ffe211070f26fe16b 0000-0001-7795-453X Hassan Eshkiki Hassan Eshkiki true false a88a07c43b3e80f27cb96897d1bc2534 Xiuyi Fan Xiuyi Fan true false 557f93dfae240600e5bd4398bf203821 0000-0002-2945-3519 Benjamin Mora Benjamin Mora true false 2021-11-02 MACS 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. Conference Paper/Proceeding/Abstract 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) 841 845 IEEE 978-1-6654-0899-8 978-1-6654-0898-1 1082-3409 2375-0197 21 12 2021 2021-12-21 10.1109/ictai52525.2021.00134 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2022-01-12T17:04:22.8138740 2021-11-02T16:14:27.6918854 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Marcin Kapcia 1 Hassan Eshkiki 0000-0001-7795-453X 2 Jamie Duell 3 Xiuyi Fan 4 Shangming Zhou 5 Benjamin Mora 0000-0002-2945-3519 6 58534__21409__69ea74efe29f4576ba3d54dc6ed1b503.pdf 58534.pdf 2021-11-02T16:18:10.4247581 Output 2443408 application/pdf Accepted Manuscript true true eng
title ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics
spellingShingle ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics
Hassan Eshkiki
Xiuyi Fan
Benjamin Mora
title_short ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics
title_full ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics
title_fullStr ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics
title_full_unstemmed ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics
title_sort ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics
author_id_str_mv c9972b26a83de11ffe211070f26fe16b
a88a07c43b3e80f27cb96897d1bc2534
557f93dfae240600e5bd4398bf203821
author_id_fullname_str_mv c9972b26a83de11ffe211070f26fe16b_***_Hassan Eshkiki
a88a07c43b3e80f27cb96897d1bc2534_***_Xiuyi Fan
557f93dfae240600e5bd4398bf203821_***_Benjamin Mora
author Hassan Eshkiki
Xiuyi Fan
Benjamin Mora
author2 Marcin Kapcia
Hassan Eshkiki
Jamie Duell
Xiuyi Fan
Shangming Zhou
Benjamin Mora
format Conference Paper/Proceeding/Abstract
container_title 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
container_start_page 841
publishDate 2021
institution Swansea University
isbn 978-1-6654-0899-8
978-1-6654-0898-1
issn 1082-3409
2375-0197
doi_str_mv 10.1109/ictai52525.2021.00134
publisher IEEE
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description 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.
published_date 2021-12-21T08:06:45Z
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score 11.087994

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