Journal article
Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
- Abstract:
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Background
Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.
Methods
We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (... Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
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(Preview, Version of record, pdf, 1.3MB, Terms of use)
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- Publisher copy:
- 10.1186/s12874-022-01577-x
Authors
Bibliographic Details
- Publisher:
- BioMed Central
- Journal:
- BMC Medical Research Methodology More from this journal
- Volume:
- 22
- Article number:
- 101
- Publication date:
- 2022-04-08
- Acceptance date:
- 2022-03-18
- DOI:
- EISSN:
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1471-2288
- Pmid:
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35395724
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1250589
- Local pid:
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pubs:1250589
- 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
- Licence:
- CC Attribution (CC BY)
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