Personalized　diagnosis　prediction　based　on　electronic　health　records　(EHR)　of　patients　is　a　promising　yet　challenging　task　for　AI　in　healthcare.　Existing　studies　typically　ignore　the　heterogeneity　of　diseases　across　different　patients.　For　example,　diabetes　can　have　different　complications　across　different.　patients　(e.g.,　hyperlipidemia　and　circulatory　disorder),　which　requires　personalized　diagnoses　and　treatments.　Specifically,　existing　models　fail　to　consider　1)　varying　severity　of　the　same　diseases　for　different　patients,　2)　complex　interactions　among　syndromic　diseases,　and　3)　dynamic　progression　of　chronic　diseases.　In　this　work,　we　propose　to　perform　personalized　diagnosis　prediction　based　on　EHR　data　via　capturing　disease　severity,　interaction,　and　progression.　In　particular,　we　enable　personalized　disease　representations　via　severity-driven　embeddings　at　the　disease　level.　Then,　at　the　visit　level,　we　propose　to　capture　higher-order　interactions　among　diseases　that　can　collectively　affect　patients＇　health　status　via　hypergraphbased　aggregation;　at　the　patient　level,　we　devise　a　personalized　generative　model　based　on　neural　ordinary　differential　equations　to　capture　the　continuous　-time　disease　progressions　underlying　discrete　and　incomplete　visits.　Extensive　experiments　on　two　realworld　EHR　datasets　show　significant　performance　gains　brought　by　our　approach,　yielding　average　improvements　of　10.70%　for　diagnosis　prediction　over　state-of-the-art　competitors.