Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model
This work addresses predictive healthcare for CLL patients by integrating relational data, but it is incremental as it builds on existing time-series methods with a specific graph-based enhancement.
The paper tackled the problem of predicting treatment initiation for Chronic Lymphocytic Leukemia patients by incorporating patient-clinician relational data into a time-sensitive model, resulting in a 5% improvement in area under precision-recall curve over a baseline.
Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare. However, the common relations among patients (e.g., share the same doctor) were rarely considered. In this paper, we represent patients and clinicians relations by bipartite graphs addressing for example from whom a patient get a diagnosis. We then solve for the top eigenvectors of the graph Laplacian, and include the eigenvectors as latent representations of the similarity between patient-clinician pairs into a time-sensitive prediction model. We conducted experiments using real-world data to predict the initiation of first-line treatment for Chronic Lymphocytic Leukemia (CLL) patients. Results show that relational similarity can improve prediction over multiple baselines, for example a 5% incremental over long-short term memory baseline in terms of area under precision-recall curve.