Building Structured Personal Health Records from Photographs of Printed Medical Records.
Identifieur interne : 000009 ( PubMed/Corpus ); précédent : 000008; suivant : 000010Building Structured Personal Health Records from Photographs of Printed Medical Records.
Auteurs : Xiang Li ; Gang Hu ; Xiaofei Teng ; Guotong XieSource :
- AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium [ 1942-597X ] ; 2015.
Abstract
Personal health records (PHRs) provide patient-centric healthcare by making health records accessible to patients. In China, it is very difficult for individuals to access electronic health records. Instead, individuals can easily obtain the printed copies of their own medical records, such as prescriptions and lab test reports, from hospitals. In this paper, we propose a practical approach to extract structured data from printed medical records photographed by mobile phones. An optical character recognition (OCR) pipeline is performed to recognize text in a document photo, which addresses the problems of low image quality and content complexity by image pre-processing and multiple OCR engine synthesis. A series of annotation algorithms that support flexible layouts are then used to identify the document type, entities of interest, and entity correlations, from which a structured PHR document is built. The proposed approach was applied to real world medical records to demonstrate the effectiveness and applicability.
PubMed: 26958219
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Annual Symposium proceedings / AMIA Symposium. AMIA Symposium</title><idno type="eISSN">1942-597X</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Personal health records (PHRs) provide patient-centric healthcare by making health records accessible to patients. In China, it is very difficult for individuals to access electronic health records. Instead, individuals can easily obtain the printed copies of their own medical records, such as prescriptions and lab test reports, from hospitals. In this paper, we propose a practical approach to extract structured data from printed medical records photographed by mobile phones. An optical character recognition (OCR) pipeline is performed to recognize text in a document photo, which addresses the problems of low image quality and content complexity by image pre-processing and multiple OCR engine synthesis. A series of annotation algorithms that support flexible layouts are then used to identify the document type, entities of interest, and entity correlations, from which a structured PHR document is built. The proposed approach was applied to real world medical records to demonstrate the effectiveness and applicability.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="In-Data-Review"><PMID Version="1">26958219</PMID><DateCreated><Year>2016</Year><Month>03</Month><Day>09</Day></DateCreated><DateRevised><Year>2016</Year><Month>03</Month><Day>15</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1942-597X</ISSN><JournalIssue CitedMedium="Internet"><Volume>2015</Volume><PubDate><Year>2015</Year></PubDate></JournalIssue><Title>AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium</Title><ISOAbbreviation>AMIA Annu Symp Proc</ISOAbbreviation></Journal><ArticleTitle>Building Structured Personal Health Records from Photographs of Printed Medical Records.</ArticleTitle><Pagination><MedlinePgn>833-42</MedlinePgn></Pagination><Abstract><AbstractText>Personal health records (PHRs) provide patient-centric healthcare by making health records accessible to patients. In China, it is very difficult for individuals to access electronic health records. Instead, individuals can easily obtain the printed copies of their own medical records, such as prescriptions and lab test reports, from hospitals. In this paper, we propose a practical approach to extract structured data from printed medical records photographed by mobile phones. An optical character recognition (OCR) pipeline is performed to recognize text in a document photo, which addresses the problems of low image quality and content complexity by image pre-processing and multiple OCR engine synthesis. A series of annotation algorithms that support flexible layouts are then used to identify the document type, entities of interest, and entity correlations, from which a structured PHR document is built. The proposed approach was applied to real world medical records to demonstrate the effectiveness and applicability.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Xiang</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>IBM Research Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hu</LastName><ForeName>Gang</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>IBM Research Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Teng</LastName><ForeName>Xiaofei</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>IBM Research Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xie</LastName><ForeName>Guotong</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>IBM Research Beijing, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>11</Month><Day>05</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>AMIA Annu Symp Proc</MedlineTA><NlmUniqueID>101209213</NlmUniqueID><ISSNLinking>1559-4076</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>J Am Med Inform Assoc. 2006 Jan-Feb;13(1):30-9</RefSource><PMID Version="1">16221939</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Am Med Inform Assoc. 2006 Mar-Apr;13(2):121-6</RefSource><PMID Version="1">16357345</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>BMJ. 2014;349:g6805</RefSource><PMID Version="1">25395433</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Proc AMIA Symp. 2002;:56-60</RefSource><PMID Version="1">12463786</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Am Med Inform Assoc. 2012 Jun;19(e1):e90-5</RefSource><PMID Version="1">21890871</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Stud Health Technol Inform. 2012;180:751-5</RefSource><PMID Version="1">22874292</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Am Med Inform Assoc. 2011 Jul-Aug;18(4):515-22</RefSource><PMID Version="1">21672914</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>AMIA Annu Symp Proc. 2011;2011:824-33</RefSource><PMID Version="1">22195140</PMID></CommentsCorrections></CommentsCorrectionsList><OtherID Source="NLM">PMC4765700</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="ecollection"><Year>2015</Year><Month></Month><Day></Day></PubMedPubDate><PubMedPubDate PubStatus="epublish"><Year>2015</Year><Month>11</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>3</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>1</Month><Day>1</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>1</Month><Day>1</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">26958219</ArticleId><ArticleId IdType="pmc">PMC4765700</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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