Identification of Medical Coding Errors and Assessment of Representation Approaches for Medical Notes Using Artificial Intelligence

Electronic health records (EHR) information have excellent possible to benefit public health, medical research and health care administration. The application is restricted by the intricacy of the multi-source health data in different forms. In recent years, artificial intelligence and deep learning methods have actually been applied and shown to have appealing enhancement in utilizing EHR health information for patient subtyping, future disease forecast, learning of medical idea embedding and so on, yet in need of broader and additional investigations. This short article, initially, proposes a data-driven method using a stack of logistic regression, assistance vector machine and random forest to assist determine medical coding errors and demonstrates its expediency through experiments on EHR data of Diagnosis Related Groups.

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