However, the obtained vectors tend to be high dimensional and sparse, which makes it hard to calculate patient similarity accurately. The similarity between patients is expressed by calculating the similarity or dissimilarity between the corresponding vectors of medical events, thereby completing the patient similarity measurement. Existing patient similarity search methods retrieve medical events associated with patients from Electronic Health Record (EHR) data and map them to vectors. Patient similarity search is a fundamental and important task in artificial intelligence-assisted medicine service, which is beneficial to medical diagnosis, such as making accurate predictions for similar diseases and recommending personalized treatment plans. 3Shandong Provincial Key Laboratory of Software Engineering, Shandong University, Jinan, China.2Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China.1School of Software, Shandong University, Jinan, China. ![]() Hao-zhe Huang 1, Xu-dong Lu 1 *, Wei Guo 1,2, Xin-bo Jiang 1,3, Zhong-min Yan 1 and Shi-peng Wang 1
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