…
comment: The repository administrator has not yet configured an RDF license.
primary topic: A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions

A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
type: AcademicArticle
type: Article
abstract: 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 peopleâ��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.
date: 2021
volume: 13
issue: 12
is part of: Symmetry
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

MDPI
type: Organization

Symmetry
type: Collection
issn: 20738994

W. Ali
type: Person
given name: W.
family name: Ali

T.A.A. Abdullah
type: Person
given name: T.A.A.
family name: Abdullah

M.S.M. Zahid
type: Person
given name: M.S.M.
family name: Zahid

HTML Summary of #14149 A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions
format: text/html
primary topic: A review of interpretable ml in healthcare: Taxonomy, applications, challenges, and future directions