A Study on Automatic Generation of Chinese Discharge Summary

Discharge summary, which summarizes a patient's health information during hospitalization, is very important for transferring information between the hospitalist and primary care physician. Discharge summary writing is a necessary but time-consuming job for physicians. Automatically generating discharge summaries using information technology is helpful to physicians, but challenging as discharge summaries are typically long and contain amounts of information to outline patient's reason for admission, labtests, examinations, diagnostic findings, treatments, and medication care plan. In this study, we propose a framework based on deep learning methods for automatic discharge summary generation. In the framework, we split information of discharge summary into two parts: 1) history information such as reason for admission, admission diagnosis; 2) outcome information such as discharge diagnosis and medication care plan, and deploy different neural networks to generate them separately. Hierarchical sequence labeling methods are proposed to select key sentences from existing documents, e.g., admission notes, progress notes, examination reports as the history information, and multi-task learning methods to predict the outcomes, e.g., diagnosis and medication care plan. Experiments on a Chinese corpus show that our approach has the ability to generate effective discharge summaries.