Constructing　large-scaled　medical　knowledge　graphs　(MKGs)　can　significantly　boost　healthcare　applications　for　medical　surveillance,　bring　much　attention　from　recent　research.　An　essential　step　in　constructing　large-scale　MKG　is　extracting　information　from　medical　reports.　Recently,　information　extraction　techniques　have　been　proposed　and　show　promising　performance　in　biomedical　information　extraction.　However,　these　methods　only　consider　limited　types　of　entity　and　relation　due　to　the　noisy　biomedical　text　data　with　complex　entity　correlations.　Thus,　they　fail　to　provide　enough　information　for　constructing　MKGs　and　restrict　the　downstream　applications.　To　address　this　issue,　we　propose　Biomedical　Information　Extraction　(BioIE),　a　hybrid　neural　network　to　extract　relations　from　biomedical　text　and　unstructured　medical　reports.　Our　model　utilizes　a　multi-head　attention　enhanced　graph　convolutional　network　(GCN)　to　capture　the　complex　relations　and　context　information　while　resisting　the　noise　from　the　data.　We　evaluate　our　model　on　two　major　biomedical　relationship　extraction　tasks,　chemical-disease　relation　(CDR)　and　chemical-protein　interaction　(CPI),　and　a　cross-hospital　pan-cancer　pathology　report　corpus.　The　results　show　that　our　method　achieves　superior　performance　than　baselines.　Furthermore,　we　evaluate　the　applicability　of　our　method　under　a　transfer　learning　setting　and　show　that　BioIE　achieves　promising　performance　in　processing　medical　text　from　different　formats　and　writing　styles.　©　2021　IEEE.