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Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical
Colin George Wyllie Weaver1, Robert B Basmadjian1, Tyler Williamson1
1Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
JMIR Research Protocols
|March 3, 2022
Summary
This systematic review protocol outlines an assessment of reporting quality for machine learning in clinical prediction models. It aims to improve standardization and completeness in reporting these AI-driven medical tools.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Clinical Prediction Modeling
Background:
- Machine learning and AI are increasingly used in medicine, leading to more clinical prediction models.
- However, reporting of machine learning-specific aspects in these studies is often poor.
- There is a lack of reviews and guidelines for assessing this reporting quality.
Purpose of the Study:
- To conduct a systematic review assessing the reporting quality of machine learning-specific aspects in clinical prediction models.
- To identify areas where reporting is lacking and can be improved for better standardization.
Main Methods:
- Systematic review of studies using supervised machine learning for clinical prediction models.
- Search MEDLINE for 100 studies published in 2019.
- Utilize a novel checklist assessing unique machine learning reporting areas: modeling steps, performance, statistical methods, and model presentation.
Main Results:
- Data analysis was completed in August 2021.
- The manuscript is currently being written.
- Results are expected to be submitted to a peer-reviewed journal in early 2022.
Conclusions:
- This review will highlight deficiencies in reporting machine learning aspects in clinical prediction models.
- Findings will contribute to more standardized and complete reporting practices.
- The study aims to improve the quality and reliability of AI-driven medical tools.
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