Beyond Sequential Patterns: Rethinking Healthcare Predictions with Contextual Insights

Healthcare predictions, such as readmission prediction, stand as a cornerstone of societal well-being, exerting a profound influence on individual health outcomes and communal vitality. Existing research primarily employs advanced graph neural networks and sequential algorithms for patient modeling, with a focus on discerning the connections and sequential patterns inherent in Electronic Health Records (EHRs). However, the heterogeneity of entity interactions, the locality of EHR data, and the oversight of target relevance hinder further improvements. To address these limitations, we introduce a novel framework B eyond S equential P atterns (BSP), which facilitates precise healthcare predictions by incorporating tri-contextual information. Specifically, we establish a symptom-driven hypergraph network with four semantic hyperedges tailored to the intricacies of the healthcare scenario, such as ontology. This serves as a global context, tracking the heterogeneous entity collaboration within and across patients. Moreover, we construct an extensive knowledge graph leveraging existing medical databases and large language models. By sampling and refining knowledge subgraphs as local context, we bolster the semantic associations of medical entities from closed-set EHR data to the open world. Finally, we introduce the candidate context, an explicit entity-relation loss. It enforces the neighbor consistency between the target and the representation during optimization, thus accounting for correlations among targets. Extensive experiments and rigorous robustness analysis on five tasks derived from four large medical datasets underscore the BSP’s superiority over the leading baselines, with improvements of 11%, 3%, 11%, 3.5%, and 2% across five tasks, demonstrating the efficacy of incorporating diverse contexts.