Predicting　patients＇　risk　of　developing　certain　diseases　is　an　important　research　topic　in　healthcare.　Personalized　predictive　modeling,　which　focuses　on　building　specific　models　for　individual　patients,　has　shown　its　advantages　on　utilizing　heterogeneous　health　data　compared　to　global　models　trained　on　the　entire　population.　Personalized　predictive　models　use　information　from　similar　patient　cohorts,　in　order　to　capture　the　specific　characteristics.　Accurately　identifying　and　ranking　the　similarity　among　patients　based　on　their　historical　records　is　a　key　step　in　personalized　modeling.　The　electric　health　records　(EHRs),　which　are　irregular　sampled　and　have　varied　patient　visit　lengths,　cannot　be　directly　used　to　measure　patient　similarity　due　to　lack　of　an　appropriate　vector　representation.　In　this　paper,　we　build　a　novel　time　fusion　CNN　framework　to　simultaneously　learn　patient　representations　and　measure　pairwise　similarity.　Compared　to　a　traditional　CNN,　our　time　fusion　CNN　can　learn　not　only　the　local　temporal　relationships　but　also　the　contributions　from　each　time　interval.　Along　with　the　similarity　learning　process,　the　output　information　which　is　the　probability　distribution　is　used　to　rank　similar　patients.　Utilizing　the　similarity　scores,　we　perform　personalized　disease　predictions,　and　compare　the　effect　of　different　vector　representations　and　similarity　learning　metrics.