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Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults
BACKGROUND: Payers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. Penalized linear regression represents a practical and incremental step forward that provides transparency and interpretability within t...
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| Publicado no: | PLoS One |
|---|---|
| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6402678/ https://ncbi.nlm.nih.gov/pubmed/30840682 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0213258 |
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