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Rule-based approach for identifying assertions in clinical free-text data

Sun, Yu, Nguyen, Anthony, Sitbon, Laurianne, & Geva, Shlomo (2010) Rule-based approach for identifying assertions in clinical free-text data. In Scholer F, Trotman A , F, Turpin, A, & Trotman , A (Eds.) Proceedings of 15th Australasian Document Computing Symposium, School of Computer Science and IT, RMIT University, Melbourne, VIC, pp. 93-96.

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Abstract

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

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ID Code: 48508
Item Type: Conference Paper
Keywords: rule-based, assertion, NegEx, Context, SNOMED CT, medical concept
ISBN: 978-1-921426-80-3
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 08 Feb 2012 11:16
Last Modified: 01 Mar 2012 10:07

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