Continuous　diagnosis　prediction　based　on　multi-modal　electronic　health　records　(EHRs)　of　patients　is　a　promising　yet　challenging　task　for　AI　in　healthcare.　Existing　studies　ignore　abundant　domain　knowledge　of　diseases　(e.g.,　specific　medical　terms　and　their　interrelations)　in　textual　EHRs,　which　fails　to　accurately　predict　disease　progression　and　assist　in　sequential　diagnosis　prediction.　To　this　end,　we　first　propose　an　Expert　enhanced　neural　Ordinary　Differential　Equations　(ExpertODE)　framework　for　continuous　diagnosis　prediction.　In　particular,　we　first　propose　a　novel　Mixture　of　Language　Experts　(MoLE)　module　to　enhance　disease　embeddings　with　domain　knowledge.　Furthermore,　we　propose　a　Contrastive　Neural　Ordinary　Differential　Equation　(CNODE)　module　to　continuously　model　temporal　correlations　of　disease　progression,　and　implement　a　unified　contrastive　learning　framework　to　jointly　optimize　the　domain-based　MoLE　module　and　the　temporal-based　CNODE　module.　Extensive　experiments　on　two　real-world　textual　EHR　datasets　show　significant　performance　gains　brought　by　our　ExpertODE,　yielding　average　improvements　of　3.91%　for　diagnosis　prediction　over　state-of-the-art　competitors.　©　2024　IEEE.