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Mixed Effect Machine Learning: a framework for predicting longitudinal change in hemoglobin A1c
Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assump...
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| Publicado no: | J Biomed Inform |
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| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6495570/ https://ncbi.nlm.nih.gov/pubmed/30189255 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jbi.2018.09.001 |
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