The　combined　models　of　Graph　Neural　Network　(GNN)　and　Recurrent　Neural　Network　(RNN)　are　widely　used　for　patient　similarity　computation.　However,　these　studies　mainly　use　the　medical　concepts　to　organize　patient　graphs,　while　a　lot　of　concepts　in　Electronic　Medical　Records　(EMRs)　are　paratactic,　learning　the　temporal　information　based　on　concept　sequences　may　introduce　noise　to　similarity　computation.　To　address　this　problem,　we　propose　an　Event　Graph　Learning　Network　(EGLN)　to　learn　patient　similarity.　Specially,　we　firstly　leverage　the　trained　Event　Extraction　(EE)　model　to　obtain　the　event　elements.　Then,　aggregating　the　paratactic　concepts　of　each　medical　event　to　construct　the　event　graph　for　the　patient　to　avoid　the　negative　influence　of　nonexistent　temporal　information　between　paratactic　concepts.　Finally,　the　spatial　and　temporal　semantic　information　between　event　nodes　is　aggregated　for　similarity　computation.　We　evaluate　EGLN　leveraging　a　real-world　dataset,　and　the　experiment　results　indicate　that　our　proposed　EGLN　model　outperforms　all　baselines.　©　2024　IEEE.