From Probabilities to Prescriptions: A Unified Dependency-Aware Set Decoder for Medication and Herb Recommendation

Western medication and traditional Chinese medicine (TCM) herb recommendations differ in clinical theories, vocabularies, and safety knowledge, yet share a common set-valued prediction problem: mapping a clinical presentation to a variable-size prescription. Candidate-wise probabilities alone do not generally determine the Jaccard-optimal prescription, because the same marginals can arise from different candidate co-occurrence patterns. We therefore formulate both tasks as post-hoc set decoding and propose Dependency-Aware Prescription Selection (DAPS), a backbone-agnostic decoder for frozen probabilistic recommenders. DAPS uses two cross-fitted stages: the first calibrates candidate scores and constructs a provisional prescription, while the second refines these scores using set-conditioned context. Crucially, it separates behavioral co-selection and exclusion learned from recorded prescriptions from externally listed conflicts such as drug–drug interactions or herb incompatibilities. The refined probabilities are converted into a variable-size prescription through Jaccard-oriented cardinality selection, followed by a validation-selected global external-conflict budget. Across two electronic health record (EHR) and two TCM corpora, DAPS improves Jaccard in all 36 matched backbone–dataset evaluations, with average gains of $5.77$ and $6.24$ percentage points on the EHR and TCM corpora, respectively. Analyses characterize the sources and robustness of these gains, while highlighting the distinction between observational dependencies and external conflict knowledge.