Engineering natural language processing solutions for structured information from clinical text: extracting sentinel events from palliative care consult letters.
Despite a trend to formalize and codify medical information, natural language communications still play a prominent role in health care workflows, in particular when it comes to hand-overs between providers. Natural language processing (NLP) attempts to bridge the gap between informal, natural language information and coded, machine-interpretable data. This paper reports on a study that applies an advanced NLP method for the extraction of sentinel events in palliative care consult letters. Sentinel events are of interest to predict survival and trajectory for patients with acute palliative conditions. Our NLP method combines several novel characteristics, e.g., the consideration of topological knowledge structures sourced from an ontological terminology system (SNOMED CT). The method has been applied to the extraction of different types of sentinel events, including simple facts, temporal conditions, quantities, and degrees. A random selection of 215 anonymized consult letters was used for the study. The results of the NLP extraction were evaluated by comparison with coded sentinel event data captured independently by clinicians. The average accuracy of the automated extraction was 73.6%.
Department of Computer Science, University of Victoria, Victoria, BC, Canada.
Neil
N
Barrett
Jens H
JH
Weber-Jahnke
Vincent
V
Thai
D000416:Alberta; D057225:Data Mining / Q000379:methods*; D016347:Medical Records Systems, Computerized / Q000145:classification*; D009323:Natural Language Processing; D010166:Palliative Care / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000379:methods; D012017:Referral and Consultation / Q000145:classification*; D018571:Sentinel Surveillance; D039061:Systematized Nomenclature of Medicine; D009626:Terminology as Topic