Diagnosis　prediction　aims　to　predict　the　future　health　status　of　patients　according　to　their　historical　visit　records,　which　is　an　important　yet　challenging　task　in　healthcare　informatics.　Existing　diagnosis　prediction　approaches　mainly　employ　recurrent　neural　networks　(RNNs)　with　attention　mechanisms　to　make　predictions.　However,　these　approaches　ignore　the　importance　of　code　descriptions,　i.e.,　the　medical　definitions　of　diagnosis　codes.　We　believe　that　taking　diagnosis　code　descriptions　into　account　can　help　the　state-of-the-art　models　not　only　to　learn　meaningful　code　representations,　but　also　to　improve　the　predictive　performance.　Thus,　in　this　paper,　we　propose　a　simple,　but　general　diagnosis　prediction　framework,　which　includes　two　basic　components:　diagnosis　code　embedding　and　predictive　model.　To　learn　the　interpretable　code　embeddings,　we　apply　convolutional　neural　networks　(CNNs)　to　model　medical　descriptions　of　diagnosis　codes　extracted　from　online　medical　websites.　The　learned　medical　embedding　matrix　is　used　to　embed　the　input　visits　into　vector　representations,　which　are　fed　into　the　predictive　models.　Any　existing　diagnosis　prediction　approach　(referred　to　as　the　base　model)　can　be　cast　into　the　proposed　framework　as　the　predictive　model　(called　the　enhanced　model).　We　conduct　experiments　on　two　real　medical　datasets:　the　MIMIC-III　dataset　and　the　Heart　Failure　claim　dataset.　Experimental　results　show　that　the　enhanced　diagnosis　prediction　approaches　significantly　improve　the　prediction　performance.　©　2018　IEEE.