The Role of Explainability in Assuring Safety of Machine Learning in Healthcare-Reference-Cited by-同舟云学术

The Role of Explainability in Assuring Safety of Machine Learning in Healthcare

Published:2022-10-01 Issue:4 Volume:10 Page:1746-1760
ISSN:2168-6750
Container-title:IEEE Transactions on Emerging Topics in Computing
language:
Short-container-title:IEEE Trans. Emerg. Topics Comput.

Author:

Jia Yan¹^ORCID,McDermid John¹^ORCID,Lawton Tom²^ORCID,Habli Ibrahim¹^ORCID

Affiliation:

1. Department of Computer Science, University of York, York, U.K.

2. Bradford Royal Infirmary, Bradford Institute for Health Research, Bradford, U.K.

Funder

Bradford Teaching Hospitals NHS Foundation Trust

University of York

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Human-Computer Interaction,Information Systems,Computer Science (miscellaneous)

Link

http://xplorestaging.ieee.org/ielx7/6245516/9970409/09769937.pdf?arnumber=9769937

Reference55 articles.

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2. Regularizing black-box models for improved interpretability;plumb,2020

3. The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies;markus;J Biomed Informat,2020

4. Efficient Data Representation by Selecting Prototypes with Importance Weights

5. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

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