| Automated discovery of drug treatment patterns for endocrine therapy of breast cancer within an electronic medical record. | |
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MedLine Citation:
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PMID: 22140207 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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ObjectiveTo develop an algorithm for the discovery of drug treatment patterns for endocrine breast cancer therapy within an electronic medical record and to test the hypothesis that information extracted using it is comparable to the information found by traditional methods.MaterialsThe electronic medical charts of 1507 patients diagnosed with histologically confirmed primary invasive breast cancer.MethodsThe automatic drug treatment classification tool consisted of components for: (1) extraction of drug treatment-relevant information from clinical narratives using natural language processing (clinical Text Analysis and Knowledge Extraction System); (2) extraction of drug treatment data from an electronic prescribing system; (3) merging information to create a patient treatment timeline; and (4) final classification logic.ResultsAgreement between results from the algorithm and from a nurse abstractor is measured for categories: (0) no tamoxifen or aromatase inhibitor (AI) treatment; (1) tamoxifen only; (2) AI only; (3) tamoxifen before AI; (4) AI before tamoxifen; (5) multiple AIs and tamoxifen cycles in no specific order; and (6) no specific treatment dates. Specificity (all categories): 96.14%-100%; sensitivity (categories (0)-(4)): 90.27%-99.83%; sensitivity (categories (5)-(6)): 0-23.53%; positive predictive values: 80%-97.38%; negative predictive values: 96.91%-99.93%.DiscussionOur approach illustrates a secondary use of the electronic medical record. The main challenge is event temporality.ConclusionWe present an algorithm for automated treatment classification within an electronic medical record to combine information extracted through natural language processing with that extracted from structured databases. The algorithm has high specificity for all categories, high sensitivity for five categories, and low sensitivity for two categories. |
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Authors:
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Guergana K Savova; Janet E Olson; Sean P Murphy; Victoria L Cafourek; Fergus J Couch; Matthew P Goetz; James N Ingle; Vera J Suman; Christopher G Chute; Richard M Weinshilboum |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-12-1 |
Journal Detail:
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Title: Journal of the American Medical Informatics Association : JAMIA Volume: - ISSN: 1527-974X ISO Abbreviation: - Publication Date: 2011 Dec |
Date Detail:
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Created Date: 2011-12-5 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9430800 Medline TA: J Am Med Inform Assoc Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Mayo Clinic, Rochester, Minnesota, USA. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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