title: Extraction of events and temporal expressions from clinical narratives.
abstract: This paper addresses an important task of event and timex extraction from clinical narratives in context of the i2b2 2012 challenge. State-of-the-art approaches for event extraction use a multi-class classifier for finding the event types. However, such approaches consider each event in isolation. In this paper, we present a sentence-level inference strategy which enforces consistency constraints on attributes of those events which appear close to one another. Our approach is general and can be used for other tasks as well. We also design novel features like clinical descriptors (from medical ontologies) which encode a lot of useful information about the concepts. For timex extraction, we adapt a state-of-the-art system, HeidelTime, for use in clinical narratives and also develop several rules which complement HeidelTime. We also give a robust algorithm for date extraction. For the event extraction task, we achieved an overall F1 score of 0.71 for determining span of the events along with their attributes. For the timex extraction task, we achieved an F1 score of 0.79 for determining span of the temporal expressions. We present detailed error analysis of our system and also point out some factors which can help to improve its accuracy.
authors.affiliation: Department of Computer Science, UIUC, 201 N. Goodwin Ave, Urbana, IL 61801, United States. Electronic address: jindal2@illinois.edu.
authors.forename: Prateek
authors.initials: P
authors.lastname: Jindal
authors.affiliation: Department of Computer Science, UIUC, 201 N. Goodwin Ave, Urbana, IL 61801, United States. Electronic address: danr@illinois.edu.
authors.forename: Dan
authors.initials: D
authors.lastname: Roth
mesh_terms: D057286:Electronic Health Records; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D033262:Narration; D009323:Natural Language Processing
keywords: Electronic health records; Information extraction; Integer quadratic programmming; Named entity recognition; Natural language processing; Temporal extraction