Exploring　the　temporal　relationship　among　events　in　patient　electronic　medical　records　(EMR)　is　an　important　problem　in　biomedical　informatics　and　the　results　can　reveal　patients＇　impending　disease　conditions.　In　this　paper,　we　investigate　the　problem　of　mining　patterns　from　a　sequence　of　point　events,　i.e.,　we　only　have　the　information　on　when　the　event　happens　but　no　duration　or　numerical　value　available.　We　propose　a　whole　pipeline,　including　event　preprocessing,　pattern　mining,　and　outcome　analysis　to　mine　the　patterns　and　evaluate　their　effectiveness　and　discriminative　power.　Finally,　we　treat　those　mined　patterns　as　additional　features　and　evaluate　them　in　a　predictive　modeling　task　for　the　early　detection　of　congestive　heart　failure.　On　a　real-world　EMR　data　warehouse,　we　found　that　by　adding　those　sequential　pattern　features,　the　prediction　performance　could　be　significantly　improved　approximately　0.1.