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Development of a natural language processing system to identify timing and status of colonoscopy testing in electronic medical records
Colorectal cancer (CRC) screening rates are low despite proven benefits. We developed natural language processing (NLP) algorithms to identify temporal expressions and status indicators, such as "patient refused" or "test scheduled." The authors incorporated the algorithms into t...
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Published in: | AMIA ... Annual Symposium proceedings 2009-11, Vol.2009, p.141-141 |
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creator | Denny, Joshua C Peterson, Josh F Choma, Neesha N Xu, Hua Miller, Randolph A Bastarache, Lisa Peterson, Neeraja B |
description | Colorectal cancer (CRC) screening rates are low despite proven benefits. We developed natural language processing (NLP) algorithms to identify temporal expressions and status indicators, such as "patient refused" or "test scheduled." The authors incorporated the algorithms into the KnowledgeMap Concept Identifier system in order to detect references to completed colonoscopies within electronic text. The modified NLP system was evaluated using 200 randomly selected electronic medical records (EMRs) from a primary care population aged >/=50 years. The system detected completed colonoscopies with recall and precision of 0.93 and 0.92. The system was superior to a query of colonoscopy billing codes to determine screening status. |
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We developed natural language processing (NLP) algorithms to identify temporal expressions and status indicators, such as "patient refused" or "test scheduled." The authors incorporated the algorithms into the KnowledgeMap Concept Identifier system in order to detect references to completed colonoscopies within electronic text. The modified NLP system was evaluated using 200 randomly selected electronic medical records (EMRs) from a primary care population aged >/=50 years. The system detected completed colonoscopies with recall and precision of 0.93 and 0.92. The system was superior to a query of colonoscopy billing codes to determine screening status.</description><identifier>EISSN: 1559-4076</identifier><identifier>PMID: 20351837</identifier><language>eng</language><publisher>United States: American Medical Informatics Association</publisher><subject>Algorithms ; Colonoscopy ; Colorectal Neoplasms - diagnosis ; Early Detection of Cancer ; Electronic Health Records ; Humans ; Natural Language Processing ; Time Factors</subject><ispartof>AMIA ... 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Annual Symposium proceedings</title><addtitle>AMIA Annu Symp Proc</addtitle><description>Colorectal cancer (CRC) screening rates are low despite proven benefits. We developed natural language processing (NLP) algorithms to identify temporal expressions and status indicators, such as "patient refused" or "test scheduled." The authors incorporated the algorithms into the KnowledgeMap Concept Identifier system in order to detect references to completed colonoscopies within electronic text. The modified NLP system was evaluated using 200 randomly selected electronic medical records (EMRs) from a primary care population aged >/=50 years. The system detected completed colonoscopies with recall and precision of 0.93 and 0.92. The system was superior to a query of colonoscopy billing codes to determine screening status.</description><subject>Algorithms</subject><subject>Colonoscopy</subject><subject>Colorectal Neoplasms - diagnosis</subject><subject>Early Detection of Cancer</subject><subject>Electronic Health Records</subject><subject>Humans</subject><subject>Natural Language Processing</subject><subject>Time Factors</subject><issn>1559-4076</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNpVUMtKxDAUDYI44-gvSHauCk3TNOlGkPExwoAbXZc0ua2RNqlJOjA_4HebwVF0deHe87jnnKAlYazOypxXC3Qewnuel5yJ6gwtipwyIihfos872MHgphFsxK7DElsZZy8HPEjbz7IHPHmnIARjexz2IcKIo8NGJ4Lp9jia8XCRVuMQEzUcVJQbnHVBuSkBIMQDwlgMA6jonTUKj6CNSi4elPM6XKDTTg4BLo9zhV4f7l_Wm2z7_Pi0vt1mExF5zCreipKSlpBK11JRwouW5CLtQFcFdDUrgZYKKBSE8hSylVWrSqFEzWsNmq7QzbfuNLfpBZVCpKzN5M0o_b5x0jT_L9a8Nb3bNYUgrOQiCVwfBbz7mFO0ZjRBwZDaAjeHhlMqKOO0SMirv1a_Hj_d0y-JXYP1</recordid><startdate>20091114</startdate><enddate>20091114</enddate><creator>Denny, Joshua C</creator><creator>Peterson, Josh F</creator><creator>Choma, Neesha N</creator><creator>Xu, Hua</creator><creator>Miller, Randolph A</creator><creator>Bastarache, Lisa</creator><creator>Peterson, Neeraja B</creator><general>American Medical Informatics Association</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20091114</creationdate><title>Development of a natural language processing system to identify timing and status of colonoscopy testing in electronic medical records</title><author>Denny, Joshua C ; Peterson, Josh F ; Choma, Neesha N ; Xu, Hua ; Miller, Randolph A ; Bastarache, Lisa ; Peterson, Neeraja B</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p180t-67b8431b116d9ac3172b108843ed62ef954e34ce3e2137035ba6bc48c8979ded3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithms</topic><topic>Colonoscopy</topic><topic>Colorectal Neoplasms - diagnosis</topic><topic>Early Detection of Cancer</topic><topic>Electronic Health Records</topic><topic>Humans</topic><topic>Natural Language Processing</topic><topic>Time Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Denny, Joshua C</creatorcontrib><creatorcontrib>Peterson, Josh F</creatorcontrib><creatorcontrib>Choma, Neesha N</creatorcontrib><creatorcontrib>Xu, Hua</creatorcontrib><creatorcontrib>Miller, Randolph A</creatorcontrib><creatorcontrib>Bastarache, Lisa</creatorcontrib><creatorcontrib>Peterson, Neeraja B</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>AMIA ... 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subjects | Algorithms Colonoscopy Colorectal Neoplasms - diagnosis Early Detection of Cancer Electronic Health Records Humans Natural Language Processing Time Factors |
title | Development of a natural language processing system to identify timing and status of colonoscopy testing in electronic medical records |
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