The　accumulation　and　explosive　growth　of　the　electronic　medical　records　(EMRs)　make　the　name　entity　recognition　(NER)　technologies　become　critical　for　the　meaningful　use　of　EMR　data　and　then　the　practice　of　evidence-based　medicine.　The　dominate　NER　approaches　use　the　distributed　representation　of　the　words　and　characters　to　build　deep　learning-based　NER　models.　However,　for　the　task　of　biomedical　named　entity　recognition,　there　are　a　large　amount　of　complicated　medical　terminologies　that　are　composed　of　multiple　words.　Splitting　these　terminologies　to　learn　the　word　and　character　embeddings　might　cause　semantic　ambiguities.　In　this　paper,　we　treat　each　medical　terminology　as　a　concept　and　propose　a　concept-enhanced　named　entity　recognition　model　(CNER),　where　the　features　from　three　different　granularities　(i.e.,　concept,　word,　and　character)　are　combined　together　for　bio-NER.　The　extensive　experiments　are　conducted　on　two　real-world　corpora:　fully　labeled　corpus　and　partially　labeled　corpus.　CNER　achieves　the　highest　F1　score　(fully　labeled　corpus:　precision　=　88.23,　recall　=　88.29,　and　F1　=　88.26;　partially　labeled　corpus:　precision　=　87.03,　recall　=　88.19,　and　F1　=　87.61)　by　outperforming　the　baseline　CW-BLSTM-CRF　approach　for　0.58%　and　1.15%　respectively,　which　demonstrates　the　effectiveness　of　the　proposed　approach.