As　an　important　family　of　learning　problems　in　healthcare　domain,　patient　similarity　learning　has　received　much　attention　in　recent　years.　Patient　similarity　learning　aims　to　measure　the　similarity　between　a　pair　of　patients　according　to　their　historical　clinical　information,　which　helps　to　improve　the　clinical　predictions　of　the　patient　of　interest.　Although　patient　similarity　learning　has　achieved　tremendous　success　in　many　real-world　applications,　the　lack　of　transparency　behind　the　behavior　of　the　learned　patient　similarity　model　impedes　users　from　trusting　the　predicted　results,　which　hampers　its　further　applications　in　the　real　world.　To　tackle　this　problem,　in　this　paper,　we　investigate　how　to　enable　interpretation　in　patient　similarity　learning　and　propose　a　global　interpretation　method　for　patient　similarity　learning.　Based　on　the　proposed　global　interpretation　method,　we　can　identify　a　minimal　sufficient　subset　of　data　features　that　are　sufficient　in　themselves　to　justify　the　global　predictions　made　by　the　well-trained　patient　similarity　model.　The　identified　minimal　sufficient　feature　subset　can　help　us　to　better　understand　the　overall　behaviors　of　the　learned　model　across　different　subpopulations　of　patients.　We　also　conduct　experiments　on　real-world　datasets　to　evaluate　the　performance　of　the　proposed　global　interpretation　method.