## 1 Introduction  
Venous thromboembolism ( VTE) is a group of diseases including deep vein thrombosis and pulmonary embolism. VTE is one of the three most common vascular diseases, and its mortality is second only to tumors and myocardial infarction [1]. Surveys in 32 countries show that more than half of hospitalized patients are at risk of VTE, and only half of high-risk patients have access to effective preventive measures [2]. The occurrence of VTE in hospitalized patients is often related to a series of factors such as the patient's advanced age, genetic predisposition, and surgical treatment [3], VTE is also an important cause of unexpected deaths in hospitals, and has become a serious problem faced by hospital administrators and clinical medical staff. Early identification and diagnosis can effectively reduce the risk of venous thromboembolism [4]. At present, early screening is mainly performed by using the VTE risk assessment scale. The more commonly used assessment scales are Padua, Caprini, Wells, etc. The above risk assessment scales all use the linear weighting method, assigning different scores to different risk factors in the assessment scale, finally adding the scores to obtain the risk assessment score, and then refer to VTE guidelines to determine a patient's current VTE risk level and what precautions should be taken. However, there are too many items in the clinically used VTE assessment scale. Taking the Caprini scale as an example, there are as many as 37 items. The way of filling in the form manually not only increases the workload of medical staff, but also uses the evaluation method of a table item, which will lead to different results due to the different experience of medical staff, making them therefore prone to inconsistency in evaluation, resulting in patients being unable to obtain correct preventive measures. With the development of artificial intelligence technology and EHR storage technology [5], some scholars use artificial intelligence technology to intelligently assess the risk of VET. For example, [PERSON] et al. [6] divided the risk level of VTE into 5 categories, and extracted 35 risk factors from the Caprini assessment scale; 35 risk factors were input into a multilayer perceptron feedforward neural network to predict the VET risk level of hospitalized patients. Inconsistencies in assessments can be addressed, but risk factors entered into the model need to be captured by the physician from the electronic medical record. [PERSON] et al. [7] developed a VTE diagnosis decision support system based on knowledge representation and logical reasoning, supplemented by case-based reasoning. Case-based reasoning method requires time-consuming and laborious construction of case base. [PERSON] et al. [8] proposed an ontology-based assessment model for VTE, constructing an ontology including comprehensive information such as symptoms, diagnoses, drugs, and surgeries, and using rule-based and greedy matching methods to extract data from electronic medical records risk factors, and finally using machine learning to judge a risk level. This method can identify the information related to VTE from the electronic medical record, but it takes time and effort for domain experts to build the ontology set. [PERSON] et al. [9] developed an algorithm to automatically calculate the VTE risk score for the Padua assessment scale and calculate the Padua assessment scale score by obtaining the risk factors related to the Padua assessment scale from the admission record. This method requires building an Enterprise Data Warehouse (EDW) to identify risk factors. In Table 1, we summarize the recent work. We can find that although the current VTE intelligent assessment method uses artificial intelligence technology, the acquisition of risk factors still relies on the extraction of medical staff, and the workload of medical staff has not been greatly liberated. As a precious medical resource, electronic medical records record various information of the patient's medical treatment process.