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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...
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| I publikationen: | BMC Med Res Methodol |
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| Huvudupphovsmän: | , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
BioMed Central
2019
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6937754/ https://ncbi.nlm.nih.gov/pubmed/31888507 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12874-019-0863-0 |
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