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Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach
BACKGROUND: Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the “gold standard” reflecting the actual diseases...
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| Gepubliceerd in: | JMIR Med Inform |
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| Hoofdauteurs: | , , , , , , , , , , , , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
JMIR Publications
2020
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7775195/ https://ncbi.nlm.nih.gov/pubmed/33331828 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.2196/22649 |
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