Discovering　latent　topics　from　biomedical　documents　has　become　a　pivotal　task　in　many　biomedical　text　mining　applications.　Medical　Subject　Headings　(MeSH)　terms,　which　are　curated　by　human　experts,　provide　highly　precise　keyword　representations　for　biomedical　documents.　However,　the　performance　of　conventional　topic　models　on　MeSH　documents　is　usually　unsatisfying　due　to　the　limited　length　of　individual　MeSH　documents.　In　this　paper,　we　propose　a　novel　topic　model　for　MeSH　documents　using　MeSH　embeddings.　The　proposed　topic　model　is　able　to　overcome　the　lack　of　context　information　problem　in　MeSH　documents　by　1)　exploiting　the　rich　term-level　co-occurrence　patterns　instead　of　the　sparse　document-level　co-occurrence　patterns,　and　2)　incorporating　additional　MeSH　semantics　in　MeSH　embeddings　learned　from　a　large　external　biomedical　knowledge　base.　Experimental　result　on　a　real-world　biomedical　dataset　shows　the　efficacy　of　the　proposed　model　in　discovering　coherent　topics　from　MeSH　documents.　©　2019　IEEE.