Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach

Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach

BackgroundDiabetes 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” reflecti...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: JMIR Publications 2020-12-01
Series:JMIR Medical Informatics
Online Access:http://medinform.jmir.org/2020/12/e22649/

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http://medinform.jmir.org/2020/12/e22649/

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