Automatically　extract　relations　between　medical　entity　pairs　is　fundamental　in　biomedical　research.　Since　the　annotated　dataset　is　very　expensive,　distant　supervision　provides　an　efficient　solution　to　reduce　the　cost　of　annotation　by　utilizing　rough　corpus　labeled　with　semantic　knowledge　base.　However,　two　same　entities　mentioned　in　different　sentences　may　express　different　relations,　it　is　difficult　for　the　traditional　distant　supervision　methods　to　distinguish　these　different　relations.　In　this　paper,　we　propose　a　new　model　for　biomedical　relation　extraction　in　Chinese　EMRs.　First,　the　distant　supervision　is　used　for　coarse-grained　relation　labeling.　Then,　the　fine-grained　relations　are　annotated　initially　by　measuring　the　distance　between　the　contextual　information　of　the　relation　instance　to　the　semantic　profile　of　each　candidate　fine-grained　relation　category.　Finally,　the　high　confidence　fine-grained　relation　instances　are　selected　as　initial　training　set　for　PCNN　model,　in　addition,　a　bootstrap　learning　is　introduced　in　the　training　process　to　enhance　the　performance　of　fine-grained　relation　extraction.　Experiments　conducted　on　a　real-word　dataset　and　the　results　show　that　our　method　outperforms　all　baseline　systems.　　©　2022　IEEE.