With　the　rapid　development　of　healthcare　industry,　the　overwhelming　amounts　of　electronic　health　records　(EHRs)　have　been　well　documented　and　shared　by　healthcare　institutions　and　practitioners.　It　is　important　to　take　advantage　of　EHR　data　to　develop　an　effective　disease　risk　management　model　that　not　only　predicts　the　progression　of　the　disease,　but　also　provides　a　candidate　list　of　informative　risk　factors　(RFs)　in　order　to　prevent　the　disease.　Although　EHRs　are　valuable　sources　due　to　the　comprehensive　patient　information,　it　is　difficult　to　pinpoint　the　underlying　causes　of　the　disease　in　order　to　assess　the　risk　of　a　patient　in　developing　a　target　disease.　Because　of　the　entangled　EHR　data,　it　is　also　challenging　to　discriminate　between　patients　suffering　from　the　disease　and　without　the　disease　for　the　purpose　of　selecting　RFs　that　cause　the　disease.　To　tackle　these　challenges,　we　propose　a　disease　memory　(DM)　framework　which　can　extract　the　integrated　features　by　modeling　the　relationships　among　RFs　and　more　importantly　between　RFs　and　the　target　disease　by　establishing　a　deep　graphical　model　with　two　types　of　labels.　The　variants　of　DM　can　model　characteristics　for　patients　with　disease　and　without　disease　respectively　via　training　deep　networks　with　different　samples.　Experiments　on　a　real　bone　disease　data　set　show　that　the　proposed　framework　can　successfully　predict　the　bone　disease　and　select　the　informative　RFs　that　are　beneficial　and　useful　to　aid　clinical　decision　support.　Most　of　the　selected　RFs　are　validated　by　medical　literature　and　some　new　RFs　will　attract　interests　in　medical　research.　The　stable　and　promising　performance　on　evaluation　metrics　confirms　the　effectiveness　of　our　model.　©　2013　IEEE.