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Medical activities, such as diagnoses, medicine treatments, and laboratorytests, as well as temporal relations between these activities are the basicconcepts in clinical research. However, existing relational data model onelectronic medical records (EMRs) lacks explicit and accurate semanticdefinitions of these concepts. It leads to the inconvenience of queryconstruction and the inefficiency of query execution where multi-table joinqueries are frequently required. In this paper, we propose a patient eventgraph (PatientEG) model to capture the characteristics of EMRs. We respectivelydefine five types of medical entities, five types of medical events and fivetypes of temporal relations. Based on the proposed model, we also construct aPatientEG dataset with 191,294 events, 3,429 distinct entities, and 545,993temporal relations using EMRs from Shanghai Shuguang hospital. To help tonormalize entity values which contain synonyms, hyponymies, and abbreviations,we link them with the Chinese biomedical knowledge graph. With the help ofPatientEG dataset, we are able to conveniently perform complex queries forclinical research such as auxiliary diagnosis and therapeutic effectivenessanalysis. In addition, we provide a SPARQL endpoint to access PatientEG datasetand the dataset is also publicly available online. Also, we list severalillustrative SPARQL queries on our website.

PatientEG Dataset: Bringing Event Graph Model with Temporal Relations to Electronic Medical Records