  Medical activities, such as diagnoses, medicine treatments, and laboratory
tests, as well as temporal relations between these activities are the basic
concepts in clinical research. However, existing relational data model on
electronic medical records (EMRs) lacks explicit and accurate semantic
definitions of these concepts. It leads to the inconvenience of query
construction and the inefficiency of query execution where multi-table join
queries are frequently required. In this paper, we propose a patient event
graph (PatientEG) model to capture the characteristics of EMRs. We respectively
define five types of medical entities, five types of medical events and five
types of temporal relations. Based on the proposed model, we also construct a
PatientEG dataset with 191,294 events, 3,429 distinct entities, and 545,993
temporal relations using EMRs from Shanghai Shuguang hospital. To help to
normalize entity values which contain synonyms, hyponymies, and abbreviations,
we link them with the Chinese biomedical knowledge graph. With the help of
PatientEG dataset, we are able to conveniently perform complex queries for
clinical research such as auxiliary diagnosis and therapeutic effectiveness
analysis. In addition, we provide a SPARQL endpoint to access PatientEG dataset
and the dataset is also publicly available online. Also, we list several
illustrative SPARQL queries on our website.
