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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Detalles Bibliográficos
Autores principales: Rashidian, Sina, Abell-Hart, Kayley, Hajagos, Janos, Moffitt, Richard, Lingam, Veena, Garcia, Victor, Tsai, Chao-Wei, Wang, Fusheng, Dong, Xinyu, Sun, Siao, Deng, Jianyuan, Gupta, Rajarsi, Miller, Joshua, Saltz, Joel, Saltz, Mary
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775195/
https://www.ncbi.nlm.nih.gov/pubmed/33331828
http://dx.doi.org/10.2196/22649

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775195/
https://www.ncbi.nlm.nih.gov/pubmed/33331828
http://dx.doi.org/10.2196/22649

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