Predicting　patients＇　risk　of　developing　certain　diseases　is　an　important　research　topic　in　healthcare.　Accurately　identifying　and　ranking　the　similarity　among　patients　based　on　their　historical　records　is　a　key　step　in　personalized　healthcare.　The　electric　health　records　(EHRs),　which　are　irregularly　sampled　and　have　varied　patient　visit　lengths,　cannot　be　directly　used　to　measure　patient　similarity　due　to　the　lack　of　an　appropriate　representation.　Moreover,　there　needs　an　effective　approach　to　measure　patient　similarity　on　EHRs.　In　this　paper,　we　propose　two　novel　deep　similarity　learning　frameworks　which　simultaneously　learn　patient　representations　and　measure　pairwise　similarity.　We　use　a　convolutional　neural　network　(CNN)　to　capture　local　important　information　in　EHRs　and　then　feed　the　learned　representation　into　triplet　loss　or　softmax　cross　entropy　loss.　After　training,　we　can　obtain　pairwise　distances　and　similarity　scores.　Utilizing　the　similarity　information,　we　then　perform　disease　predictions　and　patient　clustering.　Experimental　results　show　that　CNN　can　better　represent　the　longitudinal　EHR　sequences,　and　our　proposed　frameworks　outperform　state-of-the-art　distance　metric　learning　methods.