Development of an optical character recognition pipeline for handwritten form fields from an electronic health record
Identifieur interne : 000114 ( Ncbi/Merge ); précédent : 000113; suivant : 000115Development of an optical character recognition pipeline for handwritten form fields from an electronic health record
Auteurs : Luke V. Rasmussen [États-Unis] ; Peggy L. Peissig [États-Unis] ; Catherine A. Mccarty [États-Unis] ; Justin Starren [États-Unis]Source :
- Journal of the American Medical Informatics Association : JAMIA [ 1067-5027 ] ; 2011.
Abstract
Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms.
We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms.
The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%.
While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.
Url:
DOI: 10.1136/amiajnl-2011-000182
PubMed: 21890871
PubMed Central: 3392858
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Peissig</name><affiliation wicri:level="2"><nlm:aff id="aff1">Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin</wicri:regionArea><placeName><region type="state">Wisconsin</region></placeName></affiliation></author><author><name sortKey="Mccarty, Catherine A" sort="Mccarty, Catherine A" uniqKey="Mccarty C" first="Catherine A" last="Mccarty">Catherine A. Mccarty</name><affiliation wicri:level="2"><nlm:aff id="aff2">Essentia Institute of Rural Health, Duluth, Minnesota, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Essentia Institute of Rural Health, Duluth, Minnesota</wicri:regionArea><placeName><region type="state">Minnesota</region></placeName></affiliation></author><author><name sortKey="Starren, Justin" sort="Starren, Justin" uniqKey="Starren J" first="Justin" last="Starren">Justin Starren</name><affiliation wicri:level="2"><nlm:aff id="aff1">Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin</wicri:regionArea><placeName><region type="state">Wisconsin</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="aff3">Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, Illinois, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, Illinois</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">21890871</idno><idno type="pmc">3392858</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392858</idno><idno type="RBID">PMC:3392858</idno><idno type="doi">10.1136/amiajnl-2011-000182</idno><date when="2011">2011</date><idno type="wicri:Area/Pmc/Corpus">000121</idno><idno type="wicri:Area/Pmc/Curation">000121</idno><idno type="wicri:Area/Pmc/Checkpoint">000132</idno><idno type="wicri:Area/Ncbi/Merge">000114</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Development of an optical character recognition pipeline for handwritten form fields from an electronic health record</title><author><name sortKey="Rasmussen, Luke V" sort="Rasmussen, Luke V" uniqKey="Rasmussen L" first="Luke V" last="Rasmussen">Luke V. Rasmussen</name><affiliation wicri:level="2"><nlm:aff id="aff1">Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin</wicri:regionArea><placeName><region type="state">Wisconsin</region></placeName></affiliation></author><author><name sortKey="Peissig, Peggy L" sort="Peissig, Peggy L" uniqKey="Peissig P" first="Peggy L" last="Peissig">Peggy L. Peissig</name><affiliation wicri:level="2"><nlm:aff id="aff1">Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin</wicri:regionArea><placeName><region type="state">Wisconsin</region></placeName></affiliation></author><author><name sortKey="Mccarty, Catherine A" sort="Mccarty, Catherine A" uniqKey="Mccarty C" first="Catherine A" last="Mccarty">Catherine A. Mccarty</name><affiliation wicri:level="2"><nlm:aff id="aff2">Essentia Institute of Rural Health, Duluth, Minnesota, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Essentia Institute of Rural Health, Duluth, Minnesota</wicri:regionArea><placeName><region type="state">Minnesota</region></placeName></affiliation></author><author><name sortKey="Starren, Justin" sort="Starren, Justin" uniqKey="Starren J" first="Justin" last="Starren">Justin Starren</name><affiliation wicri:level="2"><nlm:aff id="aff1">Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin</wicri:regionArea><placeName><region type="state">Wisconsin</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="aff3">Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, Illinois, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, Illinois</wicri:regionArea><placeName><region type="state">Illinois</region></placeName></affiliation></author></analytic><series><title level="j">Journal of the American Medical Informatics Association : JAMIA</title><idno type="ISSN">1067-5027</idno><idno type="eISSN">1527-974X</idno><imprint><date when="2011">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms.</p></sec><sec><title>Methods</title><p>We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms.</p></sec><sec><title>Observations</title><p>The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%.</p></sec><sec><title>Discussion</title><p>While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.</p></sec></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Am Med Inform Assoc</journal-id><journal-id journal-id-type="iso-abbrev">J Am Med Inform Assoc</journal-id><journal-id journal-id-type="publisher-id">jamia</journal-id><journal-id journal-id-type="hwp">amiajnl</journal-id><journal-title-group><journal-title>Journal of the American Medical Informatics Association : JAMIA</journal-title></journal-title-group><issn pub-type="ppub">1067-5027</issn><issn pub-type="epub">1527-974X</issn><publisher><publisher-name>BMJ Group</publisher-name><publisher-loc>BMA House, Tavistock Square, London, WC1H 9JR</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">21890871</article-id><article-id pub-id-type="pmc">3392858</article-id><article-id pub-id-type="publisher-id">amiajnl-2011-000182</article-id><article-id pub-id-type="doi">10.1136/amiajnl-2011-000182</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research and Applications</subject></subj-group><series-title>FOCUS on clinical research informatics</series-title></article-categories><title-group><article-title>Development of an optical character recognition pipeline for handwritten form fields from an electronic health record</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Rasmussen</surname><given-names>Luke V</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Peissig</surname><given-names>Peggy L</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>McCarty</surname><given-names>Catherine A</given-names></name><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Starren</surname><given-names>Justin</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff3">3</xref></contrib></contrib-group><aff id="aff1"><label>1</label>Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA</aff><aff id="aff2"><label>2</label>Essentia Institute of Rural Health, Duluth, Minnesota, USA</aff><aff id="aff3"><label>3</label>Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, Illinois, USA</aff><author-notes><corresp><bold>Correspondence to</bold> Luke V Rasmussen, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, BIRC/ML8, Marshfield, WI 54449, USA; <email>rasmussen.luke@mcrf.mfldclin.edu</email></corresp><fn fn-type="other"><p>Preliminary results from this work were presented as an abstract at the 2009 AMIA Summit on Translational Bioinformatics</p></fn></author-notes><pub-date pub-type="epub"><day>2</day><month>9</month><year>2011</year></pub-date><pub-date pub-type="ppub"><month>6</month><year>2012</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>6</month><year>2013</year></pub-date><pmc-comment> PMC Release delay is 12 months and 0 days and was based on the
<volume>19</volume><issue>e1</issue><fpage>e90</fpage><lpage>e95</lpage><history><date date-type="received"><day>13</day><month>2</month><year>2011</year></date><date date-type="accepted"><day>16</day><month>6</month><year>2011</year></date></history><permissions><copyright-statement>© 2012, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.</copyright-statement><copyright-year>2012</copyright-year></permissions><self-uri xlink:title="pdf" xlink:type="simple" xlink:href="amiajnl-2011-000182.pdf"></self-uri><abstract><sec><title>Background</title><p>Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms.</p></sec><sec><title>Methods</title><p>We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms.</p></sec><sec><title>Observations</title><p>The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%.</p></sec><sec><title>Discussion</title><p>While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.</p></sec></abstract><kwd-group><kwd>Luke</kwd><kwd>bioinformatics</kwd></kwd-group></article-meta></front></pmc><affiliations><list><country><li>États-Unis</li></country><region><li>Illinois</li><li>Minnesota</li><li>Wisconsin</li></region></list><tree><country name="États-Unis"><region name="Wisconsin"><name sortKey="Rasmussen, Luke V" sort="Rasmussen, Luke V" uniqKey="Rasmussen L" first="Luke V" last="Rasmussen">Luke V. Rasmussen</name></region><name sortKey="Mccarty, Catherine A" sort="Mccarty, Catherine A" uniqKey="Mccarty C" first="Catherine A" last="Mccarty">Catherine A. Mccarty</name><name sortKey="Peissig, Peggy L" sort="Peissig, Peggy L" uniqKey="Peissig P" first="Peggy L" last="Peissig">Peggy L. Peissig</name><name sortKey="Starren, Justin" sort="Starren, Justin" uniqKey="Starren J" first="Justin" last="Starren">Justin Starren</name><name sortKey="Starren, Justin" sort="Starren, Justin" uniqKey="Starren J" first="Justin" last="Starren">Justin Starren</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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