Relation　extraction　is　a　necessary　step　in　obtaining　information　from　electronic　medical　records.　The　deep　learning　methods　for　relation　extraction　are　primarily　based　on　word2vec　and　convolutional　or　recurrent　neural　network.　However,　word　vectors　generated　by　word2vec　are　static　and　cannot　well　reflect　the　different　meanings　of　polysemy　in　different　contexts　and　the　feature　extraction　ability　of　RNN　(Recurrent　Neural　Network)　is　not　good　enough.　At　the　same　time,　the　BERT　(Bidirectional　Encoder　Representations　from　Transformers)　pre-trained　language　model　has　achieved　excellent　results　in　many　natural　language　processing　tasks.　In　this　paper,　we　propose　a　medical　relation　extraction　model　based　on　BERT.　We　combine　the　information　of　the　whole　sentence　obtained　from　the　pre-train　language　model　with　the　corresponding　information　of　two　medical　entities　to　complete　relation　extraction　task.　The　experimental　data　were　obtained　from　the　Chinese　electronic　medical　records　provided　by　a　hospital　in　Beijing.　Experimental　results　on　electronic　medical　records　show　that　our　model＇s　accuracy,　precision,　recall,　and　F1-score　reach　67.37%,　69.54%,　67.38%,　68.44%,　which　are　higher　than　other　three　methods.　Because　named　entity　recognition　task　is　the　premise　of　relation　extraction,　we　will　combine　the　model　with　named　entity　recognition　in　the　future　work.　©　2020　ACM.