A study of the transferability of influenza case detection systems between two large healthcare systems
This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are...
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| Veröffentlicht in: | PloS one 2017-04, Vol.12 (4), p.e0174970-e0174970 |
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| creator | Ye, Ye Wagner, Michael M Cooper, Gregory F Ferraro, Jeffrey P Su, Howard Gesteland, Per H Haug, Peter J Millett, Nicholas E Aronis, John M Nowalk, Andrew J Ruiz, Victor M López Pineda, Arturo Shi, Lingyun Van Bree, Rudy Ginter, Thomas Tsui, Fuchiang |
| description | This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases.
A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance.
Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p |
| doi_str_mv | 10.1371/journal.pone.0174970 |
| format | Article |
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A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance.
Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task.
We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0174970</identifier><identifier>PMID: 28380048</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Adult ; Aged ; Alignment ; Analysis ; Arthritis ; Artificial intelligence ; Assaying ; Bayes Theorem ; Bayesian analysis ; Bioinformatics ; CAI ; Child ; Child, Preschool ; Children ; Classification ; Classifiers ; Clinical decision making ; Colleges & universities ; Computer and Information Sciences ; Computer assisted instruction ; Computer programs ; Confidence intervals ; Correlation analysis ; Decision making ; Decision Support Techniques ; Delivery of Health Care ; Detection equipment ; Diagnosis ; Disease ; Disease transmission ; Draper protein ; Electronic Health Records ; Electronic medical records ; Emergency Service, Hospital ; Engineering and Technology ; Environment models ; Epidemics ; Genetics ; Health care ; Health informatics ; Hospitals ; Humans ; Identification methods ; Incidence ; Infant ; Infant, Newborn ; Infections ; Influenza ; Influenza, Human - diagnosis ; Informatics ; Laboratories ; Lakes ; Language ; Learning algorithms ; Machine Learning ; Mathematical models ; Medical centers ; Medical records ; Medicine and Health Sciences ; Methods ; Middle Aged ; Natural Language Processing ; Outbreaks ; Pediatrics ; Physical Sciences ; Probability theory ; Public health ; Regulations ; Reproducibility of Results ; Research and Analysis Methods ; Rheumatoid arthritis ; Salts ; Software ; Studies ; Surveillance ; Technology Transfer ; Viruses ; World Wide Web ; Young Adult</subject><ispartof>PloS one, 2017-04, Vol.12 (4), p.e0174970-e0174970</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Ye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Ye et al 2017 Ye et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-a9fafef375fde6ff7610b92b70a051f37228f0a89d8cf7302b3d1cdfe9aaf9d93</citedby><cites>FETCH-LOGICAL-c692t-a9fafef375fde6ff7610b92b70a051f37228f0a89d8cf7302b3d1cdfe9aaf9d93</cites><orcidid>0000-0002-1138-9846</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5381795/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5381795/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28380048$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ye, Ye</creatorcontrib><creatorcontrib>Wagner, Michael M</creatorcontrib><creatorcontrib>Cooper, Gregory F</creatorcontrib><creatorcontrib>Ferraro, Jeffrey P</creatorcontrib><creatorcontrib>Su, Howard</creatorcontrib><creatorcontrib>Gesteland, Per H</creatorcontrib><creatorcontrib>Haug, Peter J</creatorcontrib><creatorcontrib>Millett, Nicholas E</creatorcontrib><creatorcontrib>Aronis, John M</creatorcontrib><creatorcontrib>Nowalk, Andrew J</creatorcontrib><creatorcontrib>Ruiz, Victor M</creatorcontrib><creatorcontrib>López Pineda, Arturo</creatorcontrib><creatorcontrib>Shi, Lingyun</creatorcontrib><creatorcontrib>Van Bree, Rudy</creatorcontrib><creatorcontrib>Ginter, Thomas</creatorcontrib><creatorcontrib>Tsui, Fuchiang</creatorcontrib><title>A study of the transferability of influenza case detection systems between two large healthcare systems</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases.
A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance.
Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task.
We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Alignment</subject><subject>Analysis</subject><subject>Arthritis</subject><subject>Artificial intelligence</subject><subject>Assaying</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bioinformatics</subject><subject>CAI</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Children</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Clinical decision making</subject><subject>Colleges & universities</subject><subject>Computer and Information Sciences</subject><subject>Computer assisted instruction</subject><subject>Computer programs</subject><subject>Confidence intervals</subject><subject>Correlation analysis</subject><subject>Decision making</subject><subject>Decision Support Techniques</subject><subject>Delivery of Health Care</subject><subject>Detection equipment</subject><subject>Diagnosis</subject><subject>Disease</subject><subject>Disease transmission</subject><subject>Draper protein</subject><subject>Electronic Health Records</subject><subject>Electronic medical records</subject><subject>Emergency Service, Hospital</subject><subject>Engineering and Technology</subject><subject>Environment models</subject><subject>Epidemics</subject><subject>Genetics</subject><subject>Health care</subject><subject>Health informatics</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Incidence</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Infections</subject><subject>Influenza</subject><subject>Influenza, Human - diagnosis</subject><subject>Informatics</subject><subject>Laboratories</subject><subject>Lakes</subject><subject>Language</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Mathematical models</subject><subject>Medical centers</subject><subject>Medical records</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Natural Language Processing</subject><subject>Outbreaks</subject><subject>Pediatrics</subject><subject>Physical Sciences</subject><subject>Probability theory</subject><subject>Public health</subject><subject>Regulations</subject><subject>Reproducibility of Results</subject><subject>Research and Analysis Methods</subject><subject>Rheumatoid arthritis</subject><subject>Salts</subject><subject>Software</subject><subject>Studies</subject><subject>Surveillance</subject><subject>Technology Transfer</subject><subject>Viruses</subject><subject>World Wide Web</subject><subject>Young 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Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ye, Ye</au><au>Wagner, Michael M</au><au>Cooper, Gregory F</au><au>Ferraro, Jeffrey P</au><au>Su, Howard</au><au>Gesteland, Per H</au><au>Haug, Peter J</au><au>Millett, Nicholas E</au><au>Aronis, John M</au><au>Nowalk, Andrew J</au><au>Ruiz, Victor M</au><au>López Pineda, Arturo</au><au>Shi, Lingyun</au><au>Van Bree, Rudy</au><au>Ginter, Thomas</au><au>Tsui, Fuchiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A study of the transferability of influenza case detection systems between two large healthcare systems</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-04-05</date><risdate>2017</risdate><volume>12</volume><issue>4</issue><spage>e0174970</spage><epage>e0174970</epage><pages>e0174970-e0174970</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases.
A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance.
Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task.
We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28380048</pmid><doi>10.1371/journal.pone.0174970</doi><tpages>e0174970</tpages><orcidid>https://orcid.org/0000-0002-1138-9846</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2017-04, Vol.12 (4), p.e0174970-e0174970 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1884474384 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Adolescent Adult Aged Alignment Analysis Arthritis Artificial intelligence Assaying Bayes Theorem Bayesian analysis Bioinformatics CAI Child Child, Preschool Children Classification Classifiers Clinical decision making Colleges & universities Computer and Information Sciences Computer assisted instruction Computer programs Confidence intervals Correlation analysis Decision making Decision Support Techniques Delivery of Health Care Detection equipment Diagnosis Disease Disease transmission Draper protein Electronic Health Records Electronic medical records Emergency Service, Hospital Engineering and Technology Environment models Epidemics Genetics Health care Health informatics Hospitals Humans Identification methods Incidence Infant Infant, Newborn Infections Influenza Influenza, Human - diagnosis Informatics Laboratories Lakes Language Learning algorithms Machine Learning Mathematical models Medical centers Medical records Medicine and Health Sciences Methods Middle Aged Natural Language Processing Outbreaks Pediatrics Physical Sciences Probability theory Public health Regulations Reproducibility of Results Research and Analysis Methods Rheumatoid arthritis Salts Software Studies Surveillance Technology Transfer Viruses World Wide Web Young Adult |
| title | A study of the transferability of influenza case detection systems between two large healthcare systems |
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