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Many computational models were proposed to extract temporal patterns fromclinical time series for each patient and among patient group for predictivehealthcare. However, the common relations among patients (e.g., share the samedoctor) were rarely considered. In this paper, we represent patients andclinicians relations by bipartite graphs addressing for example from whom apatient get a diagnosis. We then solve for the top eigenvectors of the graphLaplacian, and include the eigenvectors as latent representations of thesimilarity between patient-clinician pairs into a time-sensitive predictionmodel. We conducted experiments using real-world data to predict the initiationof first-line treatment for Chronic Lymphocytic Leukemia (CLL) patients.Results show that relational similarity can improve prediction over multiplebaselines, for example a 5% incremental over long-short term memory baseline interms of area under precision-recall curve.

Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model