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Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers

Jamie Duell Orcid Logo, Xiuyi Fan, Monika Seisenberger Orcid Logo

Lecture Notes in Computer Science, Volume: 13515, Pages: 411 - 420

Swansea University Authors: Xiuyi Fan, Monika Seisenberger Orcid Logo

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DOI (Published version): 10.1007/978-3-031-16564-1_39

Abstract

Local explanations aim to provide transparency for individual instances and their associated predictions. The need for local explanations is prominent for high-risk domains such as finance, law and health care. We propose a new model-agnostic framework for local explanations ``Polynomial Adaptive Lo...

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Published in: Lecture Notes in Computer Science
ISBN: 9783031165634 9783031165641
ISSN: 0302-9743 1611-3349
Published: Cham Springer International Publishing 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60605
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first_indexed 2022-07-22T18:41:59Z
last_indexed 2023-01-13T19:20:51Z
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spelling 2022-11-17T13:59:15.6954271 v2 60605 2022-07-22 Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers a88a07c43b3e80f27cb96897d1bc2534 Xiuyi Fan Xiuyi Fan true false d035399b2b324a63fe472ce0344653e0 0000-0002-2226-386X Monika Seisenberger Monika Seisenberger true false 2022-07-22 Local explanations aim to provide transparency for individual instances and their associated predictions. The need for local explanations is prominent for high-risk domains such as finance, law and health care. We propose a new model-agnostic framework for local explanations ``Polynomial Adaptive Local Explanations (PALE)", to combat the lack of transparency of predictions through adaptive local models. We aim to explore explanations of predictions by assessing the impact of instantaneous rate of change in each feature and the association with the resulting prediction of the local model. PALE optimises a complex black-box model and the local explanation models for each instance, providing two forms of explanations, one provided by a localised derivative of an adapting polynomial, thus emphasising instance specificity, and the latter a core interpretable logistic regression model. Conference Paper/Proceeding/Abstract Lecture Notes in Computer Science 13515 411 420 Springer International Publishing Cham 9783031165634 9783031165641 0302-9743 1611-3349 Explainable AI, Feature Importance, Electronic health records. 26 9 2022 2022-09-26 10.1007/978-3-031-16564-1_39 COLLEGE NANME COLLEGE CODE Swansea University Other 2022-11-17T13:59:15.6954271 2022-07-22T19:15:28.1789458 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jamie Duell 0000-0002-8837-7843 1 Xiuyi Fan 2 Monika Seisenberger 0000-0002-2226-386X 3 60605__24716__49ec15ecd8334b769366615f40f80c4f.pdf ISMIS_2022_Accepted_PALE.pdf 2022-07-22T19:37:57.3180842 Output 310589 application/pdf Accepted Manuscript true 2022-09-26T00:00:00.0000000 true eng 136
title Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers
spellingShingle Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers
Xiuyi Fan
Monika Seisenberger
title_short Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers
title_full Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers
title_fullStr Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers
title_full_unstemmed Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers
title_sort Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers
author_id_str_mv a88a07c43b3e80f27cb96897d1bc2534
d035399b2b324a63fe472ce0344653e0
author_id_fullname_str_mv a88a07c43b3e80f27cb96897d1bc2534_***_Xiuyi Fan
d035399b2b324a63fe472ce0344653e0_***_Monika Seisenberger
author Xiuyi Fan
Monika Seisenberger
author2 Jamie Duell
Xiuyi Fan
Monika Seisenberger
format Conference Paper/Proceeding/Abstract
container_title Lecture Notes in Computer Science
container_volume 13515
container_start_page 411
publishDate 2022
institution Swansea University
isbn 9783031165634
9783031165641
issn 0302-9743
1611-3349
doi_str_mv 10.1007/978-3-031-16564-1_39
publisher Springer International Publishing
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
document_store_str 1
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description Local explanations aim to provide transparency for individual instances and their associated predictions. The need for local explanations is prominent for high-risk domains such as finance, law and health care. We propose a new model-agnostic framework for local explanations ``Polynomial Adaptive Local Explanations (PALE)", to combat the lack of transparency of predictions through adaptive local models. We aim to explore explanations of predictions by assessing the impact of instantaneous rate of change in each feature and the association with the resulting prediction of the local model. PALE optimises a complex black-box model and the local explanation models for each instance, providing two forms of explanations, one provided by a localised derivative of an adapting polynomial, thus emphasising instance specificity, and the latter a core interpretable logistic regression model.
published_date 2022-09-26T04:18:51Z
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