Clinical Coding Based on Knowledge Enhanced Language Model and Attention Pooling

Clinical coding is obtaining a standard ICD code based on the patient’s electronic medical record (EMR) information, including diagnosis, procedure, drug list, order, etc. It is essential in the medical record information management of the hospital. However, the medical record data have problems such as low quality, insufficient data, full of non-standardized jargon, irrelevant order information, and unbalanced data distribution, which result in poor performance of clinical coding. This task is a multi-label classification problem. Based on the medical pre-trained language model and our medical knowledge engineering (MetaMed KE), we proposed a Value-Level Attention Pooling (VLAP) to build a clinical diagnostic coding framework for Chinese electronic medical records. The framework includes three components: preprocessing module, the model, and the postprocessing module. Compared to existing algorithms, our framework dramatically improves the generalization ability and accuracy in the case of insufficient data and class unbalance. Thus, our method provides a reliable automatic solution for clinical coding in hospital medical record information management.