Medication information extraction with linguistic pattern matching and semantic rules
- 1Cardiff School of Computer Science & Informatics, Cardiff University, Cardiff, UK
- 2School of Computer Science, University of Manchester, Manchester, UK
- Correspondence to Dr Irena Spasić, Cardiff School of Computer Science & Informatics, Cardiff University, 5 The Parade, Roath, Cardiff CF24 3AA, UK;
- Received 4 February 2010
- Accepted 25 June 2010
Objective This study presents a system developed for the 2009 i2b2 Challenge in Natural Language Processing for Clinical Data, whose aim was to automatically extract certain information about medications used by a patient from his/her medical report. The aim was to extract the following information for each medication: name, dosage, mode/route, frequency, duration and reason.
Design The system implements a rule-based methodology, which exploits typical morphological, lexical, syntactic and semantic features of the targeted information. These features were acquired from the training dataset and public resources such as the UMLS and relevant web pages. Information extracted by pattern matching was combined together using context-sensitive heuristic rules.
Measurements The system was applied to a set of 547 previously unseen discharge summaries, and the extracted information was evaluated against a manually prepared gold standard consisting of 251 documents. The overall ranking of the participating teams was obtained using the micro-averaged F-measure as the primary evaluation metric.
Results The implemented method achieved the micro-averaged F-measure of 81% (with 86% precision and 77% recall), which ranked this system third in the challenge. The significance tests revealed the system's performance to be not significantly different from that of the second ranked system. Relative to other systems, this system achieved the best F-measure for the extraction of duration (53%) and reason (46%).
Conclusion Based on the F-measure, the performance achieved (81%) was in line with the initial agreement between human annotators (82%), indicating that such a system may greatly facilitate the process of extracting relevant information from medical records by providing a solid basis for a manual review process.
Funding IS was supported by the BBSRC/EPSRC grant ‘the Manchester Centre for Integrative Systems Biology’. GN and JAK were partially supported by the BBSRC grant ‘Mining Term Associations from Literature to Support Knowledge Discovery in Biology’. The project described was supported in part by the i2b2 initiative, Award Number U54LM008748 from the National Library of Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Library of Medicine or the National Institutes of Health.
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.