Abstract
In this paper, we show how we have applied the Clinical Narrative Temporal Relation Ontology (CNTRO) and its associated temporal reasoning system (the CNTRO Timeline Library) to trend temporal information within medical device adverse event report narratives. 238 narratives documenting occurrences of late stent thrombosis adverse events from the Food and Drug Administration’s (FDA) Manufacturing and User Facility Device Experience (MAUDE) database were annotated and evaluated using the CNTRO Timeline Library to identify, order, and calculate the duration of temporal events. The CNTRO Timeline Library had a 95% accuracy in correctly ordering events within the 238 narratives. 41 narratives included an event in which the duration was documented, and the CNTRO Timeline Library had an 80% accuracy in correctly determining these durations. 77 narratives included documentation of a duration between events, and the CNTRO Timeline Library had a 76% accuracy in determining these durations. This paper also includes an example of how this temporal output from the CNTRO ontology can be used to verify recommendations for length of drug administration, and proposes that these same tools could be applied to other medical device adverse event narratives in order to identify currently unknown temporal trends.
Highlights
The Clinical Narrative Temporal Relation Ontology (CNTRO) [1] and its associated temporal reasoning framework (CNTRO Timeline Library) [2,3] can be used to facilitate an efficient and semi-automated temporal analysis of events documented within a narrative
The CNTRO systeminferred timeline was evaluated with a gold standard result
The CNTRO system was capable of correctly ordering each event in all but 8 of the narratives
Summary
The Clinical Narrative Temporal Relation Ontology (CNTRO) [1] and its associated temporal reasoning framework (CNTRO Timeline Library) [2,3] can be used to facilitate an efficient and semi-automated temporal analysis of events documented within a narrative. It has been shown how CNTRO can be combined with LifeFlow [4] software developed by the University of Maryland, which is capable of visualizing event sequences, such that it is possible to see patterns in the order of events within several narratives [5]. An automated temporal analysis of adverse event narratives would lead to faster identification of patterns and/or earlier prediction of a future failure, which could be used to drive improvements into the generation of medical devices
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