Identifying catheter-related events through sentence classification

Infections caused by Central Venous Catheter (CVC) use is a serious and under-reported problem in healthcare. The CVC is almost ubiquitous in critical care because it enables fast circulatory monitoring and central administration of medication and nutrition. Explicit documentation of normal CVC usage and exposure is sparse and indirect in the health record. To capture evidence about CVC-related risk of infections and complications, we have developed methods for learning classifiers for statements about CVC-related events occurring in the textual health record. We find that even with limited data it is possible to build reasonably accurate sentence classifiers for the most important events. We also find that making use of document meta information helps improve classification quality by providing additional context to a sentence. Finally, we outline some strategies on using our results for future analysis and reasoning about CVC usage intervals and CVC exposure over individual patient trajectories.