Clinical Pathway Generation from Order Histories and Discharge Summaries This paper proposes construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system. First, the system extracts subgrouping from clinical cases with the same Diagnostic Procedure Combination code (DPC) by mixture model clustering. Subgroups give different types of diseases with different temporal evolution. Then, classification models of each subgroup are constructed by the analysis of discharge summaries to capture the meaning of each subgroup. Finally, cases are classified by using the classification model and a clinical pathway is generated for each new subgroup. The proposed method was evaluated on the data sets extracted hospital information system, whose results show that plausible clinical pathways were obtained, compared with previously introduced methods.

A Study on Automatic Generation of Chinese Discharge Summary Discharge summary, which summarizes a patientu0027s health information during hospitalization, is very important for transferring information between the hospitalist and primary care physician. Discharge summary writing is a necessary but timeconsuming 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 patientu0027s 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 multitask 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.