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dc.contributor.authorDuell, J
dc.contributor.authorFan, X
dc.contributor.authorBurnett, B
dc.contributor.authorAarts, G
dc.contributor.authorZhou, S-M

eXplainable Artificial Intelligence (XAI) aims to provide intelligible explanations to users. XAI algorithms such as SHAP, LIME and Scoped Rules compute feature importance for machine learning predictions. Although XAI has attracted much research attention, applying XAI techniques in healthcare to inform clinical decision making is challenging. In this paper, we provide a comparison of explanations given by XAI methods as a tertiary extension in analysing complex Electronic Health Records (EHRs). With a large-scale EHR dataset, we compare features of EHRs in terms of their prediction importance estimated by XAI models. Our experimental results show that the studied XAI methods circumstantially generate different top features; their aberrations in shared feature importance merit further exploration from domain-experts to evaluate human trust towards XAI.

dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subjectPatient Safety
dc.subject3 Good Health and Well Being
dc.titleA Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records
dc.typeConference Proceeding
plymouth.conference-name2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
plymouth.journal2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
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
dc.rights.embargoperiodNot known
rioxxterms.funderEngineering and Physical Sciences Research Council
rioxxterms.identifier.projectUKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing
rioxxterms.typeConference Paper/Proceeding/Abstract
plymouth.funderUKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing::Engineering and Physical Sciences Research Council

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