Text data extraction for a prospective, research-focused data mart: implementation and validation
Background Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable ope...
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| Published in | BMC medical informatics and decision making Vol. 12; no. 1; p. 106 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
13.09.2012
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1472-6947 1472-6947 |
| DOI | 10.1186/1472-6947-12-106 |
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| Abstract | Background
Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of ‘machine generated’ sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review.
Methods
Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined.
Results
There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012.
Conclusions
Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. |
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| AbstractList | Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. Background: Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Methods: Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. Results: There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Conclusions: Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. Background Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of ‘machine generated’ sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Methods Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. Results There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Conclusions Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. Doc number: 106 Abstract Background: Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Methods: Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. Results: There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Conclusions: Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review.BACKGROUNDTranslational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review.Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined.METHODSEleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined.There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012.RESULTSThere was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012.Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public.CONCLUSIONSCollaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. Abstract Background Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of ‘machine generated’ sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Methods Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. Results There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Conclusions Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. Background Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of 'machine generated' sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Methods Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. Results There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Conclusions Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public. Keywords: Medical informatics, Information storage and retrieval, Information systems, Electronic health records, Automatic data processing |
| ArticleNumber | 106 |
| Audience | Academic |
| Author | Podlusky, Sofia Varga, John Just, Eric Hinchcliff, Monique Chang, Rowland W Kibbe, Warren A |
| AuthorAffiliation | 1 Department of Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, USA 2 Northwestern Medical Enterprise Data Warehouse, Chicago, USA 4 Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, USA 6 Northwestern University Feinberg School of Medicine, McGaw Pavilion, Suite M300, 240 E Huron Street, Chicago, IL, 60611, USA 5 Robert H. Lurie Comprehensive Cancer Center, Northwestern University Biomedical Informatics Center, Chicago, USA 3 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, USA |
| AuthorAffiliation_xml | – name: 2 Northwestern Medical Enterprise Data Warehouse, Chicago, USA – name: 6 Northwestern University Feinberg School of Medicine, McGaw Pavilion, Suite M300, 240 E Huron Street, Chicago, IL, 60611, USA – name: 5 Robert H. Lurie Comprehensive Cancer Center, Northwestern University Biomedical Informatics Center, Chicago, USA – name: 3 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, USA – name: 4 Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, USA – name: 1 Department of Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, USA |
| Author_xml | – sequence: 1 givenname: Monique surname: Hinchcliff fullname: Hinchcliff, Monique email: m-hinchcliff@northwestern.edu organization: Department of Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, McGaw Pavilion – sequence: 2 givenname: Eric surname: Just fullname: Just, Eric organization: Northwestern Medical Enterprise Data Warehouse – sequence: 3 givenname: Sofia surname: Podlusky fullname: Podlusky, Sofia organization: Department of Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine – sequence: 4 givenname: John surname: Varga fullname: Varga, John organization: Department of Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine – sequence: 5 givenname: Rowland W surname: Chang fullname: Chang, Rowland W organization: Department of Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine – sequence: 6 givenname: Warren A surname: Kibbe fullname: Kibbe, Warren A organization: Robert H. Lurie Comprehensive Cancer Center, Northwestern University Biomedical Informatics Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22970696$$D View this record in MEDLINE/PubMed |
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| Copyright | Hinchcliff et al.; licensee BioMed Central Ltd. 2012 COPYRIGHT 2012 BioMed Central Ltd. 2012 Hinchcliff et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2012 Hinchcliff et al.; licensee BioMed Central Ltd. 2012 Hinchcliff et al.; licensee BioMed Central Ltd. |
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| Keywords | Information storage and retrieval Automatic data processing Electronic health records Medical informatics Information systems |
| Language | English |
| License | http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| PublicationTitle | BMC medical informatics and decision making |
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| References_xml | – volume: 42 start-page: 377 issue: 2 year: 2009 end-page: 381 ident: CR12 article-title: Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support publication-title: J Biomed Inform doi: 10.1016/j.jbi.2008.08.010 – volume: 48 start-page: 38 issue: 1 year: 2009 end-page: 44 ident: CR13 article-title: Perspectives for medical informatics. 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publication-title: Int J Med Inform doi: 10.1016/S1386-5056(00)00079-4 – year: 2006 ident: CR8 article-title: A suite of natural language processing tools developed for the I2B2 project publication-title: 2006: American Medical Informatics Association – ident: CR10 – volume: 19 start-page: e119 issue: e1 year: 2012 end-page: e124 ident: CR9 article-title: Practices and perspectives on building integrated data repositories: results from a 2010 CTSA survey publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2011-000508 – volume: 2010 start-page: 712 year: 2010 end-page: 716 ident: CR4 article-title: Combining Structured and Free-text Data for Automatic Coding of Patient Outcomes publication-title: AMIA Annu Symp Proc – year: 2006 ident: CR15 publication-title: Ensuring the Inclusion of Clinical Research in the National Health Information Network, NCRR Workshop – volume: 92 start-page: e12 issue: 1 year: 2011 end-page: 15 ident: CR17 article-title: Reduced diffusion lung capacity in patients with type 2 diabetes mellitus predicts hospitalization for pneumonia publication-title: Diabetes Res Clin Pract doi: 10.1016/j.diabres.2010.12.012 – volume: 3 start-page: e3049 issue: 8 year: 2008 ident: CR5 article-title: Quantifying data quality for clinical trials using electronic data capture publication-title: PLoS One doi: 10.1371/journal.pone.0003049 – volume: 23 start-page: 581 issue: 5 year: 1980 end-page: 590 ident: CR11 article-title: Preliminary criteria for the classification of systemic sclerosis (Scleroderma) publication-title: Arthritis Rheum doi: 10.1002/art.1780230510 – volume: 30 start-page: S30 issue: 2 Suppl 71 year: 2012 ident: 591_CR16 publication-title: Clinical and experimental rheumatology – volume: 92 start-page: e12 issue: 1 year: 2011 ident: 591_CR17 publication-title: Diabetes Res Clin Pract doi: 10.1016/j.diabres.2010.12.012 – volume: 58–59 start-page: 101 year: 2000 ident: 591_CR1 publication-title: Int J Med Inform doi: 10.1016/S1386-5056(00)00079-4 – volume: 42 start-page: 377 issue: 2 year: 2009 ident: 591_CR12 publication-title: J Biomed Inform doi: 10.1016/j.jbi.2008.08.010 – volume: 48 start-page: 38 issue: 1 year: 2009 ident: 591_CR13 publication-title: Methods of information in medicine doi: 10.3414/ME9132 – volume: 10 start-page: 327 issue: 3–4 year: 2004 ident: 591_CR7 publication-title: Nat Lang Eng doi: 10.1017/S1351324904003523 – volume-title: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL'02) year: 2002 ident: 591_CR2 – ident: 591_CR10 – volume: 17 start-page: 253 issue: 3 year: 2010 ident: 591_CR6 publication-title: J Am Med Inform Assoc doi: 10.1136/jamia.2009.002295 – volume-title: 2006: American Medical Informatics Association year: 2006 ident: 591_CR8 – volume: 19 start-page: e119 issue: e1 year: 2012 ident: 591_CR9 publication-title: J Am Med Inform Assoc doi: 10.1136/amiajnl-2011-000508 – volume: 23 start-page: 581 issue: 5 year: 1980 ident: 591_CR11 publication-title: Arthritis Rheum doi: 10.1002/art.1780230510 – volume-title: Ensuring the Inclusion of Clinical Research in the National Health Information Network, NCRR Workshop year: 2006 ident: 591_CR15 – volume: 3 start-page: e3049 issue: 8 year: 2008 ident: 591_CR5 publication-title: PLoS One doi: 10.1371/journal.pone.0003049 – volume: 2010 start-page: 712 year: 2010 ident: 591_CR4 publication-title: AMIA Annu Symp Proc – volume: 82 start-page: 661 issue: 7 year: 2007 ident: 591_CR14 publication-title: Acad Med doi: 10.1097/ACM.0b013e318065be8d – start-page: 128 volume-title: Yearb Med Inform year: 2008 ident: 591_CR3 – reference: 22338601 - Clin Exp Rheumatol. 2012 Mar-Apr;30(2 Suppl 71):S30-7 – reference: 18660887 - Yearb Med Inform. 2008;:128-44 – reference: 19151882 - Methods Inf Med. 2009;48(1):38-44 – reference: 10978913 - Int J Med Inform. 2000 Sep;58-59:101-10 – reference: 17238550 - AMIA Annu Symp Proc. 2006;:931 – reference: 21237523 - Diabetes Res Clin Pract. 2011 Apr;92(1):e12-5 – reference: 18725958 - PLoS One. 2008;3(8):e3049 – reference: 21347071 - AMIA Annu Symp Proc. 2010;2010:712-6 – reference: 18929686 - J Biomed Inform. 2009 Apr;42(2):377-81 – reference: 20442142 - J Am Med Inform Assoc. 2010 May-Jun;17(3):253-64 – reference: 22437072 - J Am Med Inform Assoc. 2012 Jun;19(e1):e119-24 – reference: 7378088 - Arthritis Rheum. 1980 May;23(5):581-90 – reference: 17595562 - Acad Med. 2007 Jul;82(7):661-9 |
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| Snippet | Background
Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately,... Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data... Background Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately,... Doc number: 106 Abstract Background: Translational research typically requires data abstracted from medical records as well as data collected specifically for... Background: Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately,... BACKGROUND: Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately,... Abstract Background Translational research typically requires data abstracted from medical records as well as data collected specifically for research.... |
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| SubjectTerms | Analysis Automatic Data Processing Correspondence Data Mining - methods Data Mining - standards Data warehouses Development and progression Electronic Health Records Electronic records File servers Health Informatics Humans Information storage and retrieval Information systems Information Systems and Communication Service Management of Computing and Information Systems Medical Informatics Medical records Medical research Medicine Medicine & Public Health Medicine, Experimental Public software Pulmonary function tests Respiratory Function Tests Scleroderma (Disease) Scleroderma, Systemic Sclerosis Servers Software Software - standards Systemic scleroderma Translational Medical Research United States |
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| Title | Text data extraction for a prospective, research-focused data mart: implementation and validation |
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