Title: Identifying catheter-related events through sentence classification

Authors: Thomas Brox Røst; Christine Raaen Tvedt; Haldor Husby; Ingrid Andås Berg; Øystein Nytrø

Addresses: Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway ' Lovisenberg Diaconal University College, Oslo, Norway ' Institute of Clinical Medicine, University of Oslo, Oslo, Norway ' Knowit Experience Bergen, Bergen, Norway ' Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway

Abstract: 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.

Keywords: medical informatics; health informatics; machine learning; text classification; central venous catheter; CVC; natural language processing; catheter-related bloodstream infection; CRBSI; clinical notes; patient records.

DOI: 10.1504/IJDMB.2020.107877

International Journal of Data Mining and Bioinformatics, 2020 Vol.23 No.3, pp.213 - 233

Received: 30 Jan 2020
Accepted: 31 Jan 2020
Published online: 26 Jun 2020 *

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