Extracting Clinical entities and their assertions from Chinese Electronic Medical Records Based on Machine Learning

With the rapid growth of electronic medical records (EMRs) in China, large amounts of clinical data have been accumulated. However, limited work for extracting information from EMRs in Chinese has been conducted. In this work, using manually annotated dataset of EMRs in Chinese, we investigated the clinical Named Entities Recognition (NER) based on Conditional Random Field (CRF) and further built a Support Vector Machine (SVM) classifier to determine their assertion status and evaluate the contributions of different features for assertion classification. For Chinese clinical NER, our CRF-based classifier achieved the best F-measure of 89.07%, while the SVM-based assertion classifier achieved a maximum F-measure of 94.10%. Our work suggests that machine learning methods are helpful in NER and assertion determination for Chinese medical clinical records.