ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics
dc.contributor.author | Kapcia, M | |
dc.contributor.author | Eshkiki, H | |
dc.contributor.author | Duell, J | |
dc.contributor.author | Fan, X | |
dc.contributor.author | Zhou, Shang-Ming | |
dc.contributor.author | Mora, B | |
dc.date.accessioned | 2022-11-07T12:09:00Z | |
dc.date.available | 2022-11-07T12:09:00Z | |
dc.date.issued | 2021-11 | |
dc.identifier.isbn | 9781665408981 | |
dc.identifier.issn | 1082-3409 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/19884 | |
dc.description.abstract |
Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) algorithms have been widely discussed by the Explainable AI (XAI) community but their application 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. 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 medical case 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. | |
dc.format.extent | 841-845 | |
dc.language.iso | en | |
dc.publisher | IEEE | |
dc.subject | Explainable AI | |
dc.subject | Medical Data Analytics | |
dc.subject | Explainability | |
dc.subject | Interpretability | |
dc.subject | COVID-19 | |
dc.subject | Cancer | |
dc.title | ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics | |
dc.type | conference | |
dc.type | Conference Proceeding | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000747482300126&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.date-start | 2021-11-01 | |
plymouth.date-finish | 2021-11-03 | |
plymouth.volume | 2021-November | |
plymouth.conference-name | 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) | |
plymouth.publication-status | Published | |
plymouth.journal | 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) | |
dc.identifier.doi | 10.1109/ictai52525.2021.00134 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Health | |
plymouth.organisational-group | /Plymouth/Faculty of Health/School of Nursing and Midwifery | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dcterms.dateAccepted | 2021-09-11 | |
dc.rights.embargodate | 2022-11-10 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.funder | Engineering and Physical Sciences Research Council | |
rioxxterms.identifier.project | UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing | |
rioxxterms.versionofrecord | 10.1109/ictai52525.2021.00134 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | |
plymouth.funder | UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing::Engineering and Physical Sciences Research Council |