Biomedical　machine　reading　comprehension　aims　to　extract　the　answer　to　the　given　question　from　complex　biomedical　passages,　which　requires　the　machine　to　have　the　ability　to　process　strong　comprehension　on　natural　language.　Recent　progress　has　made　on　this　task,　but　still　severely　restricted　by　the　insufficient　training　data　due　to　the　domain-specific　nature.　To　solve　this　problem,　we　propose　a　hierarchical　question-aware　context　learning　model　trained　by　the　multi-task　transfer　learning　algorithm,　which　can　capture　the　interaction　between　the　question　and　the　passage　layer　by　layer,　with　multi-level　embeddings　to　strengthen　the　ability　of　the　language　representation.　The　multi-task　transfer　learning　algorithm　leverages　the　advantages　of　different　machine　reading　comprehension　tasks　to　improve　model　generalisation　and　robustness,　pre-training　on　multiple　large-scale　open-domain　data　sets　and　fine-tuning　on　the　target-domain　training　set.　Moreover,　data　augmentation　is　also　adopted　to　create　new　training　samples　with　various　expressions.　The　public　biomedical　data　set　collected　from　PubMed　provided　by　BioASQ　is　used　to　evaluate　the　model　performance.　The　results　show　that　our　method　is　superior　to　the　best　recent　solution　and　achieves　a　new　state　of　the　art.