Incorporating complex domain knowledge into a recommender system in the healthcare sector

In contrast to other domains, recommender systems in health sector may benefit particularly from the incorporation of medical domain knowledge, as it provides meaningful and personalised recommendations. With recent advances in the area of representation learning enabling the hierarchical embedding of health knowledge into the hyperbolic Poincaré space, this thesis proposes a recommender system for patient-doctor matchmaking based on patients’ individual health profiles and consultation history. In doing so, a dataset from a private healthcare provider is enriched with Poincaré embeddings of the ICD-9 codes. The proposed model outperforms its conventional counterpart in terms of recommendation accuracy

Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables to discover similar temporal summaries (e.g., recurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to appea