| High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. | |
| | |
MedLine Citation:
|
PMID: 20819856 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
|
OBJECTIVE: Medication information comprises a most valuable source of data in clinical records. This paper describes use of a cascade of machine learners that automatically extract medication information from clinical records. DESIGN: Authors developed a novel supervised learning model that incorporates two machine learning algorithms and several rule-based engines. MEASUREMENTS: Evaluation of each step included precision, recall and F-measure metrics. The final outputs of the system were scored using the i2b2 workshop evaluation metrics, including strict and relaxed matching with a gold standard. RESULTS: Evaluation results showed greater than 90% accuracy on five out of seven entities in the name entity recognition task, and an F-measure greater than 95% on the relationship classification task. The strict micro averaged F-measure for the system output achieved best submitted performance of the competition, at 85.65%. LIMITATIONS: Clinical staff will only use practical processing systems if they have confidence in their reliability. Authors estimate that an acceptable accuracy for a such a working system should be approximately 95%. This leaves a significant performance gap of 5 to 10% from the current processing capabilities. CONCLUSION: A multistage method with mixed computational strategies using a combination of rule-based classifiers and statistical classifiers seems to provide a near-optimal strategy for automated extraction of medication information from clinical records. |
| | |
Authors:
|
Jon Patrick; Min Li |
Related Documents
:
|
20703656 - Applying ontology techniques to develop a medication history search and alert system in... 11730196 - Delivery of erythropoietin with a needleless injection system during hemodialysis maint... 11604946 - Supporting discovery in medicine by association rule mining in medline and umls. 15871176 - A problem-based e-learning prototype system for clinical medical education. 19520676 - Economic and practical aspects of thromboprophylaxis with unfractionated and low-molecu... 19272826 - Medical malpractice charges in germany--a survey. |
Publication Detail:
|
Type: Journal Article; Research Support, N.I.H., Extramural |
Journal Detail:
|
Title: Journal of the American Medical Informatics Association : JAMIA Volume: 17 ISSN: 1527-974X ISO Abbreviation: J Am Med Inform Assoc Publication Date: 2010 Sep-Oct |
Date Detail:
|
Created Date: 2010-09-07 Completed Date: 2010-11-15 Revised Date: 2011-09-13 |
Medline Journal Info:
|
Nlm Unique ID: 9430800 Medline TA: J Am Med Inform Assoc Country: United States |
Other Details:
|
Languages: eng Pagination: 524-7 Citation Subset: IM |
Affiliation:
|
Faculty of Engineering and IT, the University of Sydney, Sydney, Australia. jonpat@it.usyd.edu.au |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
Artificial Intelligence Electronic Health Records* Humans Information Storage and Retrieval / methods* Natural Language Processing* Pharmaceutical Preparations* |
| Grant Support | |
ID/Acronym/Agency:
|
U54LM008748/LM/NLM NIH HHS |
| Chemical | |
Reg. No./Substance:
|
0/Pharmaceutical Preparations |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Community annotation experiment for ground truth generation for the i2b2 medication challenge.
Next Document: Integrating existing natural language processing tools for medication extraction from discharge summ...