Arguably　one　of　the　biggest　breakthroughs　in　medical　domain　was　the　widespread　adoption　of　medical　search　technologies.　However,　many　traditional　information　retrieval　technologies　are　performed　poorly　when　they　are　employed　in　medical　domain.　This　paper　present　a　new　approach　with　the　goal　of　advancing　the　state-of-the-art　in　medical　information　retrieval　by　placing　the　question　of　document　ranking　in　medical　domain　of　the　broader　question　of　query-document　relevance.　This　is　achieved　by　two　factors:　query　understanding　and　scoring　the　document　importance.　For　query　understanding,　we　focus　on　understand　query　intentions　by　semantic　information　(concepts　and　relations　between　them)　from　queries.　For　document　importance,　we　propose　a　novel　strategy　is　build　the　semantic　linkages　between　concepts　distributed　in　target　documents　and　reference　documents　that　considered　not　only　the　aspect　importance　but　also　the　aspect　similarity.　The　final　ranking　list　is　produced　by　integrate　above　two　factors.　Finally,　we　present　a　detailed　performance　analysis　of　our　approach　in　comparison　to　existing　models　for　medical　search,　and　the　experimental　results　show　that　our　approach　is　greatly　improve　rank　accuracy.　©　2019,　Springer　Nature　Singapore　Pte　Ltd.