Towards a single goodness metric of clinically relevant, accurate, fair and unbiased machine learning predictions of health-related quality of life-Reference-Cited by-同舟云学术

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Towards a single goodness metric of clinically relevant, accurate, fair and unbiased machine learning predictions of health-related quality of life

Published:2023-07-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES)
language:
Short-container-title:

Author:

Zrubka Zsombor¹,Hölgyesi Áron¹,Neshat Mehdi²,Nezhad Hossein Motahari¹,Mirjalili Seyedali²,Kovács Levente³,Péntek Márta¹,Gulácsi László¹

Affiliation:

1. University Research and Innovation Center Óbuda University,Health Economics Research Center,Budapest,Hungary

2. Torrens University Australia,Center for Artificial Intelligence Research and Optimisation,Brisbane,QLD,Australia,4006

3. University Research and Innovation Center, Óbuda University,Physiological Controls Research Center,Budapest,Hungary

Funder

Innovation Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10297583/10297621/10297674.pdf?arnumber=10297674

Reference34 articles.

1. Time to start using checklists for reporting artificial intelligence in health care and biomedical research: a rapid review of available tools

2. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist

3. What Determines the Shape of an EQ-5D Index Distribution?

4. Fair machine learning in healthcare: A review;feng,2022

5. Algorithmic fairness in computational medicine

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1. A scoping review of evaluation and acceptability criteria for the accuracy of cost estimates in health economic evaluations;2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES);2024-07-17

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