As　the　generation　and　accumulation　of　massive　electronic　health　records　(EHR),　how　to　effectively　extract　the　valuable　medical　information　from　EHR　has　been　a　popular　research　topic.　During　the　medical　information　extraction,　named　entity　recognition　(NER)　is　an　essential　natural　language　processing　(NLP)　task.　This　paper　presents　our　efforts　using　neural　network　approaches　for　this　task.　Based　on　the　Chinese　EHR　offered　by　CCKS　2019　and　the　Second　Affiliated　Hospital　of　Soochow　University　(SAHSU),　several　neural　models　for　NER,　including　BiLSTM,　have　been　compared,　along　with　two　pre-trained　language　models,　word2vec　and　BERT.　We　have　found　that　the　BERT-BiLSTM-CRF　model　can　achieve　approximately　75%　F1　score,　which　outperformed　all　other　models　during　the　tests.　©　2019　IEEE.