Identifying Similar Cases in Document Networks Using Cross-Reference Structures

Our objective was to explore the creation of document networks based on different thresholds of shared information and different clustering algorithms on those networks to identify document clusters describing similar clinical cases. We created networks from vaccine adverse event report sets using seven approaches for linking reports. We then applied three clustering algorithms [visualization of similarities (VOS), Louvain, k-means] to these networks and evaluated their ability to identify known clusters. The report sets included one simulated set and three sets from the Vaccine Adverse Event Reporting System; each was split into training and testing subsets. Training subsets were used to estimate parameter values for the clustering algorithms and testing subsets to evaluate clusters. We created the networks by linking reports based on shared information in the form either of individual Medical Dictionary for Regulatory Activities Preferred Terms (PTs) or of dyads, triplets, quadruplets, quintuplets, and sextuplets of PTs; we created another network by weighting the single PT network connections by Lin's information theoretic approach to similarity. We then repeated this entire process using networks based on text mining output rather than structured data. We evaluated report clustering using recall, precision, and f-measure. The VOS algorithm outperformed Louvain and k-means in general. The best weighting scheme appeared to be related to the complexity of the known cluster. For example, singleton weighting performed best for an intussusception cluster driven by a single PT. We observed marginal differences between the code- and textual-based clustering. In conclusion, our approach supported identification of similar nodes in a document network.