Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions

Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits l...

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Bibliographic Details
Published in:Journal of the American Medical Informatics Association : JAMIA 2024-04, Vol.31 (4), p.1036-1041
Main Authors: Wei, Wei-Qi, Rowley, Robb, Wood, Angela, MacArthur, Jacqueline, Embi, Peter J, Denaxas, Spiros
Format: Article
Language:English
Subjects:
Algorithms
Biomedical Research
Electronic Health Records
Phenotype
Reference Standards
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Summary:Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources. Here, we propose five independent fundamental dimensions of phenotyping algorithms-complexity, performance, efficiency, implementability, and maintenance-through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities.
ISSN:1067-5027
1527-974X
1527-974X
DOI:10.1093/jamia/ocae005