A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions

We have witnessed the impact of ML in disease diagnosis, image recognition and classifi-cation, and many more related fields. Healthcare is a sensitive field related to people�s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e.,...

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Main Authors: Abdullah, T.A.A., Zahid, M.S.M., Ali, W.
Format: Article
Published: MDPI 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121535209&doi=10.3390%2fsym13122439&partnerID=40&md5=c8beba4fbe54517efeeb8d15d238234b
http://eprints.utp.edu.my/29604/
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Institution: Universiti Teknologi Petronas
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spelling my.utp.eprints.296042022-03-25T02:10:34Z A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions Abdullah, T.A.A. Zahid, M.S.M. Ali, W. We have witnessed the impact of ML in disease diagnosis, image recognition and classifi-cation, and many more related fields. Healthcare is a sensitive field related to people�s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Fur-thermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. MDPI 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121535209&doi=10.3390%2fsym13122439&partnerID=40&md5=c8beba4fbe54517efeeb8d15d238234b Abdullah, T.A.A. and Zahid, M.S.M. and Ali, W. (2021) A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions. Symmetry, 13 (12). http://eprints.utp.edu.my/29604/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description We have witnessed the impact of ML in disease diagnosis, image recognition and classifi-cation, and many more related fields. Healthcare is a sensitive field related to people�s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Fur-thermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Abdullah, T.A.A.
Zahid, M.S.M.
Ali, W.
spellingShingle Abdullah, T.A.A.
Zahid, M.S.M.
Ali, W.
A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
author_facet Abdullah, T.A.A.
Zahid, M.S.M.
Ali, W.
author_sort Abdullah, T.A.A.
title A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
title_short A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
title_full A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
title_fullStr A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
title_full_unstemmed A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
title_sort review of interpretable ml in healthcare: taxonomy, applications, challenges, and future directions
publisher MDPI
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121535209&doi=10.3390%2fsym13122439&partnerID=40&md5=c8beba4fbe54517efeeb8d15d238234b
http://eprints.utp.edu.my/29604/
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