Background-　In　the　study　of　rules　in　pathological　changes,　most　of　the　traditional　analyses　are　from　the　static　perspective,　which　regard　cross-sectional　data　as　the　input　dataset　to　analyze　the　effect　of　non-time-varying　factors.　However,　according　to　the　clinical　experiences,　the　changes　of　physical　status　can　partly　reflect　the　disease　progression　and　the　mortality　which　have　been　ignored　in　the　existing　studies.　Thus,　based　on　the　dynamic　perspective,　by　using　the　changing　patterns　of　symptom　as　the　model　input,　longitudinal　data　can　be　utilized　to　further　explore　the　rules　in　pathological　changes　from　the　dynamic　perspective　which　is　a　novel　and　effective　solution.　Method-　The　study　proposed　a　dynamic　pattern　representation　method;　pretreated　and　transformed　the　original　dataset,　including　the　Traditional　Chinese　Medicine　(TCM)　and　the　western　medicine　clinical　longitudinal　data,　into　a　2-dimensional　matrix　composed　of　the　symptom　indexes　and　the　changing　patterns;　and　analyzed　the　influences　between　the　changing　patterns　of　symptom　and　III　stage　non-small　cell　lung　cancer　(NSCLC)　patient＇s　mortality　by　multivariate　logistic　regression.　Result-　The　predicting　accuracy　using　the　transformed　dataset　by　proposed　representation　method　is　90.7%.　Based　on　the　enter　stepwise　regression　method,　the　accuracy　increased　26.3%　and　14.5%　than　the　baseline　dataset　and　the　last　records　respectively;　based　on　the　forward　stepwise　regression　method,　the　accuracy　increased　16.7%　and　3%　than　the　baseline　dataset　and　the　last　records　respectively.　Conclusion-　The　experiment　results　indicated　that　the　proposed　data　representation　method　is　feasible　and　effective,　meanwhile,　the　proposed　novel　dynamic　perspective　appears　more　appropriate　for　the　TCM　mainly　III　stage　NSCLC　patients＇　modeling　than　the　traditional　static　method.　©　2013　Springer-Verlag.