Temporal Hypergraph for Personalized Clinical Pathway Recommendation

Abstract

Clinical pathway recommendation aims to recommend a set of treatment modalities or treatment procedures for a patient. In order to achieve personalized clinical pathway recommendation, more and more researchers try to mine similar clinical paths from the statistical features of patient-related clinical data (such as electronic health records), while ignoring the high-order interactions between patient-related medical entities, and the time-series change pattern of this high-order interaction relationship, resulting in the inability of existing methods to fully describe patient characteristics and to accurately recommend personalized clinical pathways. To solve the above problems, we propose a new personalized clinical pathway recommendation model TempHRec. To model the complex high-order relationship in clinical pathway, hypergraph technology is introduced to solve the problem that clinical events are correlated at the same time window. On this basis, we propose a temporal hypergraph, to construct a hypergraph for each timestamp with the help of a sliding time window to capture the timing information at the clinical pathway. Extensive experimental results on real-world datasets show that the proposed model achieves the best results compared to baseline methods.