[Objective]　This　paper　mines　and　analyses　patients＇temporal　and　physiological　data　to　provide　an　accurate　and　safe　reference　for　medication　plans　and　effective　support　for　doctors＇medication　decisions.　[Methods]　A　hybrid　medication　regimen　recommendation　model　that　integrates　temporal　and　vital　sign　data　has　been　proposed.　Firstly,　the　model　uses　Transformer　architecture,　Convolutional　Neural　Networks　(CNNs),　and　time-aware　methodologies　to　analyse　patients＇temporal　data　individually.　Then,　we　leverage　knowledge　graph　technology　and　Graph　Convolutional　Neural　Networks　(GCNN)　to　explore　patients＇physiological　data.　Finally,　the　model　incorporates　adverse　drug-drug　interaction　information　into　the　recommendation　process,　thereby　providing　patients　with　safe　and　effective　medication　regimens.　[Results]　An　empirical　study　was　conducted　using　a　dataset　of　patients　who　had　been　admitted　multiple　times,　drawn　from　the　MIMIC-III　dataset.　The　recommendation　model　designed　in　this　study　achieved　Jaccard　index　improvements　of　14.0%,　6.6%　and　3.7%　over　the　GRAM,　G-BERT　and　TAHDNet　models,　respectively.　Additionally,　the　F1　metric　increased　by　9.3%,　4.4%,　and　1.2%,　respectively.　The　model　achieved　the　lowest　DDI　rate.　[Limitations]　Although　the　model　considered　abnormal　signs,　it　did　not　take　into　account　the　specific　value　of　these　signs　when　learning　from　patient　data.　[Conclusions]　Integrating　and　analysing　patients＇time　series　and　vital　sign　data　enables　the　drug　recommendation　model　to　learn　the　characteristics　of　patients＇conditions　more　accurately,　facilitating　the　recommendation　of　more　precise　medication　regimens.　Furthermore,　considering　information　on　adverse　drug　interactions　when　making　recommendations　can　help　to　ensure　safer　medication　plans　for　patients.　©　2025　Chinese　Academy　of　Sciences.　All　rights　reserved.