The　widely　adoption　of　Electronic　Medical　Records　(EMRs)　causes　an　explosive　growth　of　the　medical　and　clinical　data.　It　makes　the　medical　search　technologies　become　critical　to　find　useful　patient　information　in　the　large　medical　dataset.　However,　the　high　quality　medical　search　is　a　challenging　task,　in　particular　due　to　the　inherent　complexity　and　ambiguity　of　medical　terminology.　In　this　paper,　by　exploiting　the　uncertainty　in　ambiguous　medical　queries,　we　propose　a　novel　semantic-based　approach　to　achieve　the　diversity-aware　retrieval　of　EMRs,　i.e.,　both　the　relevance　and　novelty　are　considered　for　EMR　ranking.　With　the　support　of　medical　domain　ontologies,　we　first　mine　all　the　potential　semantics　(concepts　and　relations　between　them)　from　a　user　query　and　consume　them　to　model　the　multiple　query　aspects.　Then,　we　propose　a　novel　diversification　strategy,　which　considers　not　only　the　aspect　importance　but　also　the　aspect　similarity,　to　perform　the　diversity-aware　EMR　ranking.　A　real-world　pilot　study,　which　utilizes　the　proposed　medical　search　approach　to　improve　the　second　use　of　the　EMRs,　is　reported.　We　believe　that　our　experience　can　serve　as　an　important　reference　for　the　development　of　similar　applications　in　a　medical　data　utilization　and　sharing　environment.　(C)　2014　Elsevier　B.V.　All　rights　reserved.