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Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis
BACKGROUND: Clinical research and medical practice can be advanced through the prediction of an individual’s health state, trajectory, and responses to treatments. However, the majority of current clinical risk prediction models are based on regression approaches or machine learning algorithms that...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2019
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937754/ https://www.ncbi.nlm.nih.gov/pubmed/31888507 http://dx.doi.org/10.1186/s12874-019-0863-0 |