Explainable Longitudinal Prediction with Complete Fuzzy Measures and Higher-Order Feature Interactions

Understanding longitudinal predictions requires identifying how several variables' histories jointly shape future outcomes. Explanations limited to individual variables or pairs can miss these higher-order interactions. We introduce recursive Choquet, a longitudinal model that summarizes each feature's history separately and combines the summaries through a complete fuzzy measure. Our central construction recursively learns each feature set's value from those of its immediate subsets. We prove that this construction guarantees normalization and monotonicity without restricting interaction order. We then use the Choquet integral to produce forecasts with an exact decomposition into signed feature interactions. We compare our model with boosted trees, GRU-D, and a Transformer using common prediction-explanation methods. In controlled experiments, we recover positive and negative interactions; on a higher-order synthetic data, we obtain the lowest attribution error among these predictors at every order through six under both Faith-Shap and Shapley-Taylor. Across a harmonized set of clinical cohorts containing 166,836 participants, we predict 12 biomarkers and two depression scores with standardized test mean squared error 0.5215. For depression predictions, we obtain the most stable pair and triple explanations among these predictors, with triple cosine similarity 0.677 across training seeds. Our results support higher-order interaction recovery in controlled data and repeatable clinical explanations, providing a basis for further clinical evaluation.