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The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utilize a series of support vector machine models i...
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| Publicado no: | J Biomed Inform |
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| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2015
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4988795/ https://ncbi.nlm.nih.gov/pubmed/26122527 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jbi.2015.06.010 |
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