Clinical quantitative information recognition and entity-quantity association from Chinese electronic medical records

Clinical quantitative information contains crucial measurable expressions of patients’ diseases and treatment conditions, which are commonly exist in free-text electronic medical records. Although the clinical quantitative information is of considerable significance in assisting the analysis of health care, few researches have yet focused on the topic and it remains an ongoing challenge. Focusing on Chinese electronic medical records, this paper proposed an extended Bi-LSTM-CRF model, which integrated domain knowledge information and position characteristics of quantitative information as external features to improve the effectiveness of clinical quantitative information recognition. In addition, to associate the extracted entities and quantities more effectively, this paper presented an automatic approach for entity-quantity association using machine learning strategy. Based on 1359 actual Chinese electronic medical records from burn department of a domestic public hospital, we compared our model with a number of widely-used baseline methods. The evaluation results showed that our model outperformed the baselines with an F1-measure of 94.27% for quantitative information recognition and an accuracy of 94.60% for entity-quantity association, demonstrating its effectiveness.