Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs
A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised el...
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| description | A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the 'gastroenteric' MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived 'gastroenteric' MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives. |
| doi_str_mv | 10.1371/journal.pone.0260402 |
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Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the 'gastroenteric' MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived 'gastroenteric' MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0260402</identifier><identifier>PMID: 34882714</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Animals ; Annotations ; Anomalies ; Biology and Life Sciences ; Computer and Information Sciences ; Data Curation ; Dirichlet problem ; Disease Outbreaks - veterinary ; Dog Diseases - epidemiology ; Dogs ; Electronic Health Records ; Electronic medical records ; Electronic records ; Epidemics ; Gastroenteritis - epidemiology ; Gastroenteritis - veterinary ; Graphical representations ; Health aspects ; Health surveillance ; Identification methods ; Immunization ; Kidney diseases ; Management ; Medical records ; Medicine and Health Sciences ; Modelling ; Narratives ; Outbreaks ; Population Surveillance ; Pruritus ; Respiratory diseases ; Social Sciences ; Software ; United Kingdom - epidemiology ; Unsupervised Machine Learning ; Vomiting</subject><ispartof>PloS one, 2021-12, Vol.16 (12), p.e0260402-e0260402</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Noble 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>2021 Noble et al 2021 Noble et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-aa7c51ed2e233356e480abfe537ddbe4c5152f99c5c790ca5acd5eca91e55efd3</citedby><cites>FETCH-LOGICAL-c692t-aa7c51ed2e233356e480abfe537ddbe4c5152f99c5c790ca5acd5eca91e55efd3</cites><orcidid>0000-0002-2275-2014</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/PMC8659617/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659617/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34882714$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Dórea, Fernanda C.</contributor><creatorcontrib>Noble, Peter-John Mäntylä</creatorcontrib><creatorcontrib>Appleton, Charlotte</creatorcontrib><creatorcontrib>Radford, Alan David</creatorcontrib><creatorcontrib>Nenadic, Goran</creatorcontrib><title>Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the 'gastroenteric' MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived 'gastroenteric' MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives.</description><subject>Animals</subject><subject>Annotations</subject><subject>Anomalies</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Data Curation</subject><subject>Dirichlet problem</subject><subject>Disease Outbreaks - veterinary</subject><subject>Dog Diseases - epidemiology</subject><subject>Dogs</subject><subject>Electronic Health Records</subject><subject>Electronic medical records</subject><subject>Electronic records</subject><subject>Epidemics</subject><subject>Gastroenteritis - epidemiology</subject><subject>Gastroenteritis - veterinary</subject><subject>Graphical representations</subject><subject>Health aspects</subject><subject>Health surveillance</subject><subject>Identification methods</subject><subject>Immunization</subject><subject>Kidney diseases</subject><subject>Management</subject><subject>Medical records</subject><subject>Medicine and Health Sciences</subject><subject>Modelling</subject><subject>Narratives</subject><subject>Outbreaks</subject><subject>Population Surveillance</subject><subject>Pruritus</subject><subject>Respiratory diseases</subject><subject>Social Sciences</subject><subject>Software</subject><subject>United Kingdom - 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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>Noble, Peter-John Mäntylä</au><au>Appleton, Charlotte</au><au>Radford, Alan David</au><au>Nenadic, Goran</au><au>Dórea, Fernanda C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-12-09</date><risdate>2021</risdate><volume>16</volume><issue>12</issue><spage>e0260402</spage><epage>e0260402</epage><pages>e0260402-e0260402</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the 'gastroenteric' MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived 'gastroenteric' MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34882714</pmid><doi>10.1371/journal.pone.0260402</doi><tpages>e0260402</tpages><orcidid>https://orcid.org/0000-0002-2275-2014</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | PloS one, 2021-12, Vol.16 (12), p.e0260402-e0260402 |
| issn | 1932-6203 1932-6203 |
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| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; EZB Free E-Journals; DOAJ Directory of Open Access Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Animals Annotations Anomalies Biology and Life Sciences Computer and Information Sciences Data Curation Dirichlet problem Disease Outbreaks - veterinary Dog Diseases - epidemiology Dogs Electronic Health Records Electronic medical records Electronic records Epidemics Gastroenteritis - epidemiology Gastroenteritis - veterinary Graphical representations Health aspects Health surveillance Identification methods Immunization Kidney diseases Management Medical records Medicine and Health Sciences Modelling Narratives Outbreaks Population Surveillance Pruritus Respiratory diseases Social Sciences Software United Kingdom - epidemiology Unsupervised Machine Learning Vomiting |
| title | Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T16%3A36%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Using%20topic%20modelling%20for%20unsupervised%20annotation%20of%20electronic%20health%20records%20to%20identify%20an%20outbreak%20of%20disease%20in%20UK%20dogs&rft.jtitle=PloS%20one&rft.au=Noble,%20Peter-John%20M%C3%A4ntyl%C3%A4&rft.date=2021-12-09&rft.volume=16&rft.issue=12&rft.spage=e0260402&rft.epage=e0260402&rft.pages=e0260402-e0260402&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0260402&rft_dat=%3Cgale_plos_%3EA686069987%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2608443626&rft_id=info:pmid/34882714&rft_galeid=A686069987&rft_doaj_id=oai_doaj_org_article_16872df6ecdc41a5b9604886a51b49c5&rfr_iscdi=true |