Journal article
Risk of bias of prognostic models developed using machine learning: a systematic review in oncology
- Abstract:
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Background
Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain.
Methods
We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published betwee... Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.5MB, Terms of use)
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- Publisher copy:
- 10.1186/s41512-022-00126-w
Authors
Bibliographic Details
- Publisher:
- BioMed Central
- Journal:
- Diagnostic and Prognostic Research More from this journal
- Volume:
- 6
- Article number:
- 13
- Place of publication:
- England
- Publication date:
- 2022-07-07
- Acceptance date:
- 2022-02-07
- DOI:
- EISSN:
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2397-7523
- Pmid:
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35794668
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1267185
- Local pid:
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pubs:1267185
- Deposit date:
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2022-08-14
Terms of use
- Copyright holder:
- Dhiman et al.
- Copyright date:
- 2022
- Rights statement:
- ©2022 The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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