Automatic Error Checking and Correction of Electronic Medical Records

In this paper, an effective error checking and correction method of or Chinese medical records recognized by OCR is proposed. In our research, an optimized N-gram language model based on vocabulary rather than words is adopted to correct errors, and supervised machine learning based on maximum entropy (MaxEnt) is deployed to build a model for tokenization and named entity recognition. A medical knowledge base (MKB) is established, including dictionaries of medicine, symptoms, diseases, etc., and the frequency of each word as it appeared in the study corpus. Furthermore a Knowledge Base for Error correction (KBE) is built to automatically correct high-frequency errors. With the developed approach, the accuracy rate of the electronic medical record increases from 85.20% to 95.72%, indicating an error reduction of 71.08%.