Capturing Central Venous Catheterization Events in Health Record Texts

Abstract

Infections caused by central venous catheter (CVC) use is a serious and under-reported problem. In our research we explore methods of automatically detecting CVC use from clinical documentation for quality improvement and surveillance purposes. This paper describes our initial research on this topic, where we build CVC event classifiers based on an episodes of care corpus and an annotated gold standard. After describing the available data and gold standard we then experiment with different classification algorithms and feature selection approaches. We find that even with limited data it is possible to build reasonably accurate sentence classifiers, at least for the events that are most important to us. We also find that making use of document meta information may help improve classification quality by providing additional context to a sentence. Finally, we outline some strategies on using these preliminary clinical document-centric results as a tool for future analysis and elicitation of CVC usage intervals over full patient histories.

Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text

“…Interested in capturing infections caused by central venous catheters, a nurse specializing in infection annotated 2 745 of 22 174 inspected notes (Røst et al, 2018). Before inspection, duplicate notes were removed.…”

Identifying catheter-related events through sentence classification

“…To a lesser extent, the BigMed project (NRC project 259055) also contributed. This article is an extended version of an article accepted at BIBM 2018 Røst et al (2018).…”