Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2
Summary Nationwide Children’s Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semi-structured sleep study reports. The system showed to work more efficiently than the traditiona...
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| Veröffentlicht in: | Applied clinical informatics 2015-01, Vol.6 (2), p.345-363 |
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| creator | Chen, W. Kowatch, R. Lin, S. Splaingard, M. Huang, Y. |
| description | Summary
Nationwide Children’s Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semi-structured sleep study reports. The system showed to work more efficiently than the traditional manual chart review method, and it also enabled searching capabilities that were previously not possible.
Objective:
We report on the development and implementation of the sleep disorder i2b2 cohort identification system using natural language processing of semi-structured documents.
Methods:
We developed a natural language processing approach to automatically parse concepts and their values from semi-structured sleep study documents. Two parsers were developed: a regular expression parser for extracting numeric concepts and a NLP based tree parser for extracting textual concepts. Concepts were further organized into i2b2 ontologies based on document structures and in-domain knowledge.
Results:
26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications, procedures, diagnoses, among others. The average accuracy of terminology parsing was over 83% when comparing against those by experts. The system is capable of capturing both standard and non-standard terminologies. The time for cohort identification has been reduced significantly from a few weeks to a few seconds.
Conclusion:
Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive domain specific ontologies, allows fast and effective interactive cohort identification through the i2b2 platform for research and clinical use.
Citation:
Chen W, Kowatch R, Lin S, Splaingard M, Huang Y. Interactive cohort identification of sleep disorder patients using natural language processing and i2b2. Appl Clin Inf 2015; 6: 345–363
http://dx.doi.org/10.4338/ACI-2014-11-RA-0106 |
| doi_str_mv | 10.4338/ACI-2014-11-RA-0106 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4493335</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1696682756</sourcerecordid><originalsourceid>FETCH-LOGICAL-c448t-3dc5f2e291c7638c6364dc08889f0722d1080c9747332d9f70329bd2823d06d43</originalsourceid><addsrcrecordid>eNp9kV1vFCEYhYnR2Kb2F5gYLr0Z5WuBuTHZrFU32WhT7TVh4Z1ZmllYgWniv5d1a1Nv5AaS87yHAweh15S8E5zr98vVumOEio7S7mbZEUrkM3ROtew7wpl6_uR8hi5LuSNtLSTVWr1EZ0xSRYkm52hcxwrZuhruAa_SLuWK1x5iDUNwtoYUcRrw9wnggD-GkrKHjK-b0JCCb0uII_5q65zthDc2jrMdAV_n5KD80Wz0OLAte4VeDHYqcPmwX6DbT1c_Vl-6zbfP69Vy0zkhdO24d4uBAeupU5JrJ7kU3hGtdT8QxZg_pna9Eopz5vtBtQf2W880455IL_gF-nDyPczbPXjXYrZo5pDD3uZfJtlg_lVi2Jkx3Rshes75ohm8fTDI6ecMpZp9KA6myUZIczFU9lJqphayofyEupxKyTA8XkOJObZkWkvm2JKh1NwszbGlNvXmacLHmb-dNICdgLoLsAdzl-Yc25_91_U36_SdhQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1696682756</pqid></control><display><type>article</type><title>Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2</title><source>MEDLINE</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Chen, W. ; Kowatch, R. ; Lin, S. ; Splaingard, M. ; Huang, Y.</creator><creatorcontrib>Chen, W. ; Kowatch, R. ; Lin, S. ; Splaingard, M. ; Huang, Y.</creatorcontrib><description>Summary
Nationwide Children’s Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semi-structured sleep study reports. The system showed to work more efficiently than the traditional manual chart review method, and it also enabled searching capabilities that were previously not possible.
Objective:
We report on the development and implementation of the sleep disorder i2b2 cohort identification system using natural language processing of semi-structured documents.
Methods:
We developed a natural language processing approach to automatically parse concepts and their values from semi-structured sleep study documents. Two parsers were developed: a regular expression parser for extracting numeric concepts and a NLP based tree parser for extracting textual concepts. Concepts were further organized into i2b2 ontologies based on document structures and in-domain knowledge.
Results:
26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications, procedures, diagnoses, among others. The average accuracy of terminology parsing was over 83% when comparing against those by experts. The system is capable of capturing both standard and non-standard terminologies. The time for cohort identification has been reduced significantly from a few weeks to a few seconds.
Conclusion:
Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive domain specific ontologies, allows fast and effective interactive cohort identification through the i2b2 platform for research and clinical use.
Citation:
Chen W, Kowatch R, Lin S, Splaingard M, Huang Y. Interactive cohort identification of sleep disorder patients using natural language processing and i2b2. Appl Clin Inf 2015; 6: 345–363
http://dx.doi.org/10.4338/ACI-2014-11-RA-0106</description><identifier>ISSN: 1869-0327</identifier><identifier>EISSN: 1869-0327</identifier><identifier>DOI: 10.4338/ACI-2014-11-RA-0106</identifier><identifier>PMID: 26171080</identifier><language>eng</language><publisher>Germany: Schattauer GmbH</publisher><subject>Biological Ontologies ; Cohort Studies ; Data Mining ; Humans ; Medical Informatics - methods ; Natural Language Processing ; Research Article ; Terminology as Topic ; User-Computer Interface</subject><ispartof>Applied clinical informatics, 2015-01, Vol.6 (2), p.345-363</ispartof><rights>Copyright Schattauer 2015 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-3dc5f2e291c7638c6364dc08889f0722d1080c9747332d9f70329bd2823d06d43</citedby><cites>FETCH-LOGICAL-c448t-3dc5f2e291c7638c6364dc08889f0722d1080c9747332d9f70329bd2823d06d43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4493335/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4493335/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,315,728,781,785,886,27928,27929,53795,53797</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26171080$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, W.</creatorcontrib><creatorcontrib>Kowatch, R.</creatorcontrib><creatorcontrib>Lin, S.</creatorcontrib><creatorcontrib>Splaingard, M.</creatorcontrib><creatorcontrib>Huang, Y.</creatorcontrib><title>Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2</title><title>Applied clinical informatics</title><addtitle>Appl Clin Inform</addtitle><description>Summary
Nationwide Children’s Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semi-structured sleep study reports. The system showed to work more efficiently than the traditional manual chart review method, and it also enabled searching capabilities that were previously not possible.
Objective:
We report on the development and implementation of the sleep disorder i2b2 cohort identification system using natural language processing of semi-structured documents.
Methods:
We developed a natural language processing approach to automatically parse concepts and their values from semi-structured sleep study documents. Two parsers were developed: a regular expression parser for extracting numeric concepts and a NLP based tree parser for extracting textual concepts. Concepts were further organized into i2b2 ontologies based on document structures and in-domain knowledge.
Results:
26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications, procedures, diagnoses, among others. The average accuracy of terminology parsing was over 83% when comparing against those by experts. The system is capable of capturing both standard and non-standard terminologies. The time for cohort identification has been reduced significantly from a few weeks to a few seconds.
Conclusion:
Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive domain specific ontologies, allows fast and effective interactive cohort identification through the i2b2 platform for research and clinical use.
Citation:
Chen W, Kowatch R, Lin S, Splaingard M, Huang Y. Interactive cohort identification of sleep disorder patients using natural language processing and i2b2. Appl Clin Inf 2015; 6: 345–363
http://dx.doi.org/10.4338/ACI-2014-11-RA-0106</description><subject>Biological Ontologies</subject><subject>Cohort Studies</subject><subject>Data Mining</subject><subject>Humans</subject><subject>Medical Informatics - methods</subject><subject>Natural Language Processing</subject><subject>Research Article</subject><subject>Terminology as Topic</subject><subject>User-Computer Interface</subject><issn>1869-0327</issn><issn>1869-0327</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kV1vFCEYhYnR2Kb2F5gYLr0Z5WuBuTHZrFU32WhT7TVh4Z1ZmllYgWniv5d1a1Nv5AaS87yHAweh15S8E5zr98vVumOEio7S7mbZEUrkM3ROtew7wpl6_uR8hi5LuSNtLSTVWr1EZ0xSRYkm52hcxwrZuhruAa_SLuWK1x5iDUNwtoYUcRrw9wnggD-GkrKHjK-b0JCCb0uII_5q65zthDc2jrMdAV_n5KD80Wz0OLAte4VeDHYqcPmwX6DbT1c_Vl-6zbfP69Vy0zkhdO24d4uBAeupU5JrJ7kU3hGtdT8QxZg_pna9Eopz5vtBtQf2W880455IL_gF-nDyPczbPXjXYrZo5pDD3uZfJtlg_lVi2Jkx3Rshes75ohm8fTDI6ecMpZp9KA6myUZIczFU9lJqphayofyEupxKyTA8XkOJObZkWkvm2JKh1NwszbGlNvXmacLHmb-dNICdgLoLsAdzl-Yc25_91_U36_SdhQ</recordid><startdate>20150101</startdate><enddate>20150101</enddate><creator>Chen, W.</creator><creator>Kowatch, R.</creator><creator>Lin, S.</creator><creator>Splaingard, M.</creator><creator>Huang, Y.</creator><general>Schattauer GmbH</general><general>Schattauer</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20150101</creationdate><title>Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2</title><author>Chen, W. ; Kowatch, R. ; Lin, S. ; Splaingard, M. ; Huang, Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-3dc5f2e291c7638c6364dc08889f0722d1080c9747332d9f70329bd2823d06d43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Biological Ontologies</topic><topic>Cohort Studies</topic><topic>Data Mining</topic><topic>Humans</topic><topic>Medical Informatics - methods</topic><topic>Natural Language Processing</topic><topic>Research Article</topic><topic>Terminology as Topic</topic><topic>User-Computer Interface</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, W.</creatorcontrib><creatorcontrib>Kowatch, R.</creatorcontrib><creatorcontrib>Lin, S.</creatorcontrib><creatorcontrib>Splaingard, M.</creatorcontrib><creatorcontrib>Huang, Y.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Applied clinical informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, W.</au><au>Kowatch, R.</au><au>Lin, S.</au><au>Splaingard, M.</au><au>Huang, Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2</atitle><jtitle>Applied clinical informatics</jtitle><addtitle>Appl Clin Inform</addtitle><date>2015-01-01</date><risdate>2015</risdate><volume>6</volume><issue>2</issue><spage>345</spage><epage>363</epage><pages>345-363</pages><issn>1869-0327</issn><eissn>1869-0327</eissn><abstract>Summary
Nationwide Children’s Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semi-structured sleep study reports. The system showed to work more efficiently than the traditional manual chart review method, and it also enabled searching capabilities that were previously not possible.
Objective:
We report on the development and implementation of the sleep disorder i2b2 cohort identification system using natural language processing of semi-structured documents.
Methods:
We developed a natural language processing approach to automatically parse concepts and their values from semi-structured sleep study documents. Two parsers were developed: a regular expression parser for extracting numeric concepts and a NLP based tree parser for extracting textual concepts. Concepts were further organized into i2b2 ontologies based on document structures and in-domain knowledge.
Results:
26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications, procedures, diagnoses, among others. The average accuracy of terminology parsing was over 83% when comparing against those by experts. The system is capable of capturing both standard and non-standard terminologies. The time for cohort identification has been reduced significantly from a few weeks to a few seconds.
Conclusion:
Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive domain specific ontologies, allows fast and effective interactive cohort identification through the i2b2 platform for research and clinical use.
Citation:
Chen W, Kowatch R, Lin S, Splaingard M, Huang Y. Interactive cohort identification of sleep disorder patients using natural language processing and i2b2. Appl Clin Inf 2015; 6: 345–363
http://dx.doi.org/10.4338/ACI-2014-11-RA-0106</abstract><cop>Germany</cop><pub>Schattauer GmbH</pub><pmid>26171080</pmid><doi>10.4338/ACI-2014-11-RA-0106</doi><tpages>19</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Biological Ontologies Cohort Studies Data Mining Humans Medical Informatics - methods Natural Language Processing Research Article Terminology as Topic User-Computer Interface |
| title | Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2 |
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