OBJECTIVE: While essential for patient care, information related to medication is often written as free text in clinical records and, therefore, difficult to use in computerized systems. This paper describes an approach to automatically extract medication information from clinical records, which was developed to participate in the i2b2 2009 challenge, as well as different strategies to improve the extraction. DESIGN: Our approach relies on a semantic lexicon and extraction rules as a two-phase strategy: first, drug names are recognized and, then, the context of these names is explored to extract drug-related information (mode, dosage, etc) according to rules capturing the document structure and the syntax of each kind of information. Different configurations are tested to improve this baseline system along several dimensions, particularly drug name recognition-this step being a determining factor to extract drug-related information. Changes were tested at the level of the lexicons and of the extraction rules. RESULTS: The initial system participating in i2b2 achieved good results (global F-measure of 77%). Further testing of different configurations substantially improved the system (global F-measure of 81%), performing well for all types of information (eg, 84% for drug names and 88% for modes), except for durations and reasons, which remain problematic. CONCLUSION: This study demonstrates that a simple rule-based system can achieve good performance on the medication extraction task. We also showed that controlled modifications (lexicon filtering and rule refinement) were the improvements that best raised the performance.
OBJECTIVE: While essential for patient care, information related to medication is often written as free text in clinical records and, therefore, difficult to use in computerized systems. This paper describes an approach to automatically extract medication information from clinical records, which was developed to participate in the i2b2 2009 challenge, as well as different strategies to improve the extraction. DESIGN: Our approach relies on a semantic lexicon and extraction rules as a two-phase strategy: first, drug names are recognized and, then, the context of these names is explored to extract drug-related information (mode, dosage, etc) according to rules capturing the document structure and the syntax of each kind of information. Different configurations are tested to improve this baseline system along several dimensions, particularly drug name recognition-this step being a determining factor to extract drug-related information. Changes were tested at the level of the lexicons and of the extraction rules. RESULTS: The initial system participating in i2b2 achieved good results (global F-measure of 77%). Further testing of different configurations substantially improved the system (global F-measure of 81%), performing well for all types of information (eg, 84% for drug names and 88% for modes), except for durations and reasons, which remain problematic. CONCLUSION: This study demonstrates that a simple rule-based system can achieve good performance on the medication extraction task. We also showed that controlled modifications (lexicon filtering and rule refinement) were the improvements that best raised the performance.
Authors: Susmitha Wunnava; Xiao Qin; Tabassum Kakar; Cansu Sen; Elke A Rundensteiner; Xiangnan Kong Journal: Drug Saf Date: 2019-01 Impact factor: 5.606
Authors: Sunghwan Sohn; Cheryl Clark; Scott R Halgrim; Sean P Murphy; Christopher G Chute; Hongfang Liu Journal: J Am Med Inform Assoc Date: 2014-03-17 Impact factor: 4.497
Authors: George Karystianis; Azad Dehghan; Aleksandar Kovacevic; John A Keane; Goran Nenadic Journal: J Biomed Inform Date: 2015-06-29 Impact factor: 6.317