Progressive　diagnosis　prediction　in　healthcare　is　a　promising　yet　challenging　task.　Existing　studies　usually　assume　a　pre-defined　prior　for　generating　patient　distributions　(e.g.,　Gaussian).　However,　the　inferred　approximate　posterior　can　deviate　from　the　real-world　distribution,　which　further　affects　the　modeling　of　continuous　disease　progression　over　time.　To　alleviate　such　inference　bias,　we　propose　an　enhanced　progressive　diagnostic　prediction　model　(i.e.,　ProCNF),　which　integrates　continuous　normalizing　flows　(CNF)　and　neural　ordinary　differential　equations　(ODEs)　to　achieve　more　accurate　approximations　of　patient　health　trajectories　while　capturing　the　continuity　underlying　disease　progression.　We　first　learn　patient　embeddings　with　CNF　to　construct　a　complex　posterior　approximation　of　patient　distributions.　Then,　we　devise　a　CNF-enhanced　neural　ODE　module　for　progressive　diagnostic　prediction,　which　aims　to　improve　the　modeling　of　disease　progression　for　individual　patients.　Extensive　experiments　on　two　real-world　longitudinal　EHR　datasets　show　significant　performance　gains　brought　by　our　method　over　state-of-the-art　competitors.　©　2024　Copyright　held　by　the　owner/author(s).　Publication　rights　licensed　to　ACM.