title: Automatic computation of CHA2DS2-VASc score: information extraction from clinical texts for thromboembolism risk assessment.
abstract: The CHA2DS2-VASc score is a 10-point scale which allows cardiologists to easily identify potential stroke risk for patients with non-valvular fibrillation. In this article, we present a system based on natural language processing (lexicon and linguistic modules), including negation and speculation handling, which extracts medical concepts from French clinical records and uses them as criteria to compute the CHA2DS2-VASc score. We evaluate this system by comparing its computed criteria with those obtained by human reading of the same clinical texts, and by assessing the impact of the observed differences on the resulting CHA2DS2-VASc scores. Given 21 patient records, 168 instances of criteria were computed, with an accuracy of 97.6%, and the accuracy of the 21 CHA2DS2-VASc scores was 85.7%. All differences in scores trigger the same alert, which means that system performance on this test set yields similar results to human reading of the texts.
authors.affiliation: LIMSI-CNRS, F-91043 Orsay, France.
authors.forename: Cyril
authors.initials: C
authors.lastname: Grouin
authors.forename: Louise
authors.initials: L
authors.lastname: Deléger
authors.forename: Arnaud
authors.initials: A
authors.lastname: Rosier
authors.forename: Lynda
authors.initials: L
authors.lastname: Temal
authors.forename: Olivier
authors.initials: O
authors.lastname: Dameron
authors.forename: Pascal
authors.initials: P
authors.lastname: Van Hille
authors.forename: Anita
authors.initials: A
authors.lastname: Burgun
authors.forename: Pierre
authors.initials: P
authors.lastname: Zweigenbaum
mesh_terms: D001281:Atrial Fibrillation / Q000150:complications*; D002309:Cardiology; D057286:Electronic Health Records; D006801:Humans; D007802:Language; D009323:Natural Language Processing; D018570:Risk Assessment / Q000379:methods*; D020521:Stroke / Q000209:etiology; D013923:Thromboembolism / Q000209:etiology