25th International Conference on Database Systems for Advanced Applications

Sep. 24-27, 2020, Jeju, South Korea

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Paper details

Title: Detection of Wrong Disease Information using Knowledge-based Embedding and Attention

Authors: Wei Ge, Wei Guo, Lizhen Cui, Hui Li and Lijin Liu

Abstract: International Classification of Diseases (ICD) code has always been an important component in electronic health record (EHR). The coding errors in ICD have an extremely negative effect on the subsequent analysis using EHR. Due to some diseases been viewed as a stigma, doctors, despite having made the right diagnosis and prescribed the right drugs, would choose some diseases that symptom similarity instead of the real diseases to help patients, such as using febrile convulsions instead of epilepsy. In order to detect the wrong disease information in EHR, in this paper, we propose a method using the structured information of medications to correct the code assignments. This approach is novel and useful because patients' medications must be carefully prescribed without any bias. Specifically, we employ the Knowledge-based Embedding to help medications to get better representation and the Self-Attention Mechanism to capture the relations between medications in our proposed model. We conduct experiments on a real-world dataset, which comprises more than 300,000 medical records of over 40,000 patients. The experimental results achieve 0.972 in the AUC score, which outperforms the baseline methods and has good interpretability.

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