In　clinical　medicine,　multidimensional　time　series　data　can　be　used　to　find　the　rules　of　disease　progress　by　data　mining　technology,　such　as　classification　and　prediction.　However,　in　multidimensional　time　series　data　mining　problems,　the　excessive　data　dimension　causes　the　inaccuracy　of　probability　density　distribution　to　increase　the　computational　complexity.　Besides,　information　redundancy　and　irrelevant　features　may　lead　to　high　computational　complexity　and　over-fitting　problems.　The　combination　of　these　two　factors　can　reduce　the　classification　performance.　To　reduce　computational　complexity　and　to　eliminate　information　redundancies　and　irrelevant　features,　we　improved　upon　a　multidimensional　time　series　feature　selection　method　to　achieve　dimension　reduction.　The　improved　method　selects　features　through　the　combination　of　the　Kozacbenko-Leonenko　(K-L)　information　entropy　estimation　method　for　feature　extraction　based　on　mutual　information　and　the　feature　selection　algorithm　based　on　class　separability.　We　performed　experiments　on　the　Electroencephalogram　(EEG)　dataset　for　verification　and　the　non-small　cell　lung　cancer　(NSCLC)　clinical　dataset　for　application.　The　results　show　that　with　the　comparison　of　CLeVer,　Corona　and　AGV,　respectively,　the　improved　method　can　effectively　reduce　the　dimensions　of　multidimensional　time　series　for　clinical　data.　(C)　2015　The　Authors.　Published　by　Elsevier　Ltd.