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

ABSTRACT:

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 present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate.

Objective

This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems.

Methods

We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or witho

SUBMITTER: Rashidian S 

PROVIDER: S-EPMC7775195 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature