The　goal　of　medical　dialogue　generation　is　to　produce　precise　doctor　responses　so　that　patients　can　receive　trustworthy　medical　advice.　Medical　dialogue　generation　attracts　more　and　more　attention　as　a　result　of　the　strict　requirements　for　response　accuracy.　The　majority　of　current　research,　however,　simply　extracts　the　patient＇s　status　from　the　dialogue　history,　which　is　insufficient　for　extracting　other　medical　information　from　the　medical　discourse　and　overlooks　the　key　features　of　the　dialogue　history　itself.　Even　if　these　techniques　gather　pertinent　information,　such　as　patient　symptoms　and　diseases,　they　are　still　unable　to　generate　accurate　and　instructive　answers.　To　deal　with　this　problem,　we　propose　a　dialogue　generation　model　that　can　accomplish　Heterogenous　Medical　Information　Extraction　(HMIE),　including　patient　attributes,　dialogue　topics,　and　doctor　decisions.　Through　the　attention　mechanism,　we　present　a　patient　attribute　classifier　to　comprehend　the　variety　of　patient-related　information　in　the　dialogue.　Then,　using　the　gating　mechanism,　we　suggest　a　selector　acquires　more　precise　patient　attributes　according　to　various　dialogue　context　factors.　The　dialogue　topic　locator　initially　deduces　the　dialogue　topic　and　direction　from　the　dialogue　history　to　aid　the　generation　of　the　doctor＇s　decision　before　the　doctor　diagnosis　network　reasons　the　doctor＇s　decision.　We　conduct　experiments　on　two　large　medical　dialogue　datasets,　and　a　large　number　of　experimental　results　show　that　our　model　HMIE　outperforms　the　existing　baseline.　©　2022　IEEE.