1. Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework
- Author
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Christopher Nguan, Masoom A. Haider, Jethro C.C. Kwong, Armando J. Lorenzo, Lauren Erdman, Anna Goldenberg, Louise C. McLoughlin, Monica Farcas, Mitchell G. Goldenberg, Muhammad Mamdani, Mandy Rickard, Andrew J. Hung, Luis H. Braga, Larry Goldenberg, and Girish S. Kulkarni
- Subjects
medicine.medical_specialty ,business.industry ,Urology ,media_common.quotation_subject ,Comparability ,Reproducibility of Results ,Machine learning ,computer.software_genre ,Literacy ,Machine Learning ,medicine ,Humans ,Artificial intelligence ,business ,Set (psychology) ,computer ,Interpretability ,media_common - Abstract
The Standardized Reporting of Machine Learning Applications in Urology (STREAM-URO) framework was developed to provide a set of recommendations to help standardize how machine learning studies in urology are reported. This framework serves three purposes: (1) to promote high-quality studies and streamline the peer review process; (2) to enhance reproducibility, comparability, and interpretability of results; and (3) to improve engagement and literacy of machine learning within the urological community.
- Published
- 2021
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