Medication information extraction with linguistic pattern matching and semantic rules

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.The 2009 i2b2 medication extraction challenge¹ focused on the extraction of medication-related information including: medication name (m), dosage (do), mode (mo), frequency (f), duration (du) and reason (r) from discharge summaries. In other words, free-text medical records needed to be converted into a structured form by filling a template (a data structure with the predefined slots)² with the relevant information extracted (slot fillers). For example, the following sentence:“In the past two months, she had been taking Ativan of 3–4 mg q.d. for anxiety.”should be converted automatically into a structured form as follows:m=“ativan” ‖ do=“3–4 mg” ‖ mo=“nm” ‖ f=“q.d.” ‖ du=“two months” ‖ r=“for anxiety”Note that only explicitly mentioned information was to be extracted with no attempt to map it to standardized terminology or to interpret it semantically.