Cargando…
Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation
BACKGROUND: The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, thes...
Autores principales: | Klement, William, El Emam, Khaled |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502599/ https://www.ncbi.nlm.nih.gov/pubmed/37651179 http://dx.doi.org/10.2196/48763 |
Ejemplares similares
-
Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation
por: El Emam, Khaled, et al.
Publicado: (2020) -
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
por: Luo, Wei, et al.
Publicado: (2016) -
Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation
por: Pan, Pan, et al.
Publicado: (2020) -
Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study
por: Sheng, Kaixiang, et al.
Publicado: (2020) -
Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study
por: El Emam, Khaled, et al.
Publicado: (2022)