Non-redundant association rules between diseases and medications: an automated method for knowledge base construction.
Identifieur interne : 000043 ( PubMed/Checkpoint ); précédent : 000042; suivant : 000044Non-redundant association rules between diseases and medications: an automated method for knowledge base construction.
Auteurs : François Séverac [France] ; Erik A. Sauleau [France] ; Nicolas Meyer [France] ; Hassina Lefèvre [France] ; Gabriel Nisand [France] ; Nicolas Jay [France]Source :
- BMC medical informatics and decision making [ 1472-6947 ] ; 2015.
Descripteurs français
- KwdFr :
- MESH :
- traitement médicamenteux : Maladies cardiovasculaires.
- Bases de connaissances, Dossiers médicaux électroniques, Fouille de données, Humains, Traitement du langage naturel.
English descriptors
- KwdEn :
- MESH :
- drug therapy : Cardiovascular Diseases.
- methods : Data Mining.
- Electronic Health Records, Humans, Knowledge Bases, Natural Language Processing.
Abstract
The widespread use of electronic health records (EHRs) has generated massive clinical data storage. Association rules mining is a feasible technique to convert this large amount of data into usable knowledge for clinical decision making, research or billing. We present a data driven method to create a knowledge base linking medications to pathological conditions through their therapeutic indications from elements within the EHRs.
DOI: 10.1186/s12911-015-0151-9
PubMed: 25888890
Affiliations:
- France
- Alsace (région administrative), Grand Est, Lorraine (région)
- Nancy, Strasbourg
- Nancy-Université
Links toward previous steps (curation, corpus...)
Links to Exploration step
pubmed:25888890Le document en format XML
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Sauleau</name><affiliation wicri:level="3"><nlm:affiliation>Laboratoire de Biostatistique et d'Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. ea.sauleau@unistra.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Laboratoire de Biostatistique et d'Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg</wicri:regionArea><placeName><region type="region">Grand Est</region><region type="old region">Alsace (région administrative)</region><settlement type="city">Strasbourg</settlement></placeName></affiliation></author><author><name sortKey="Meyer, Nicolas" sort="Meyer, Nicolas" uniqKey="Meyer N" first="Nicolas" last="Meyer">Nicolas Meyer</name><affiliation wicri:level="3"><nlm:affiliation>Laboratoire de Biostatistique et d'Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. nmeyer@unistra.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Laboratoire de Biostatistique et d'Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg</wicri:regionArea><placeName><region type="region">Grand Est</region><region type="old region">Alsace (région administrative)</region><settlement type="city">Strasbourg</settlement></placeName></affiliation></author><author><name sortKey="Lefevre, Hassina" sort="Lefevre, Hassina" uniqKey="Lefevre H" first="Hassina" last="Lefèvre">Hassina Lefèvre</name><affiliation wicri:level="3"><nlm:affiliation>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. hassina.lefevre@chru-strasbourg.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg</wicri:regionArea><placeName><region type="region">Grand Est</region><region type="old region">Alsace (région administrative)</region><settlement type="city">Strasbourg</settlement></placeName></affiliation></author><author><name sortKey="Nisand, Gabriel" sort="Nisand, Gabriel" uniqKey="Nisand G" first="Gabriel" last="Nisand">Gabriel Nisand</name><affiliation wicri:level="3"><nlm:affiliation>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. gabriel.nisand@chru-strasbourg.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg</wicri:regionArea><placeName><region type="region">Grand Est</region><region type="old region">Alsace (région administrative)</region><settlement type="city">Strasbourg</settlement></placeName></affiliation></author><author><name sortKey="Jay, Nicolas" sort="Jay, Nicolas" uniqKey="Jay N" first="Nicolas" last="Jay">Nicolas Jay</name><affiliation wicri:level="4"><nlm:affiliation>LORIA - Equipe Orpailleurs, Université de Nancy, Nancy, France. nicolas.jay@loria.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>LORIA - Equipe Orpailleurs, Université de Nancy, Nancy</wicri:regionArea><placeName><region type="region">Grand Est</region><region type="old region">Lorraine (région)</region><settlement type="city">Nancy</settlement></placeName><orgName type="university">Nancy-Université</orgName></affiliation></author></analytic><series><title level="j">BMC medical informatics and decision making</title><idno type="eISSN">1472-6947</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cardiovascular Diseases (drug therapy)</term><term>Data Mining (methods)</term><term>Electronic Health Records</term><term>Humans</term><term>Knowledge Bases</term><term>Natural Language Processing</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Bases de connaissances</term><term>Dossiers médicaux électroniques</term><term>Fouille de données ()</term><term>Humains</term><term>Maladies cardiovasculaires (traitement médicamenteux)</term><term>Traitement du langage naturel</term></keywords><keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Cardiovascular Diseases</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Data Mining</term></keywords><keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr"><term>Maladies cardiovasculaires</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Electronic Health Records</term><term>Humans</term><term>Knowledge Bases</term><term>Natural Language Processing</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Bases de connaissances</term><term>Dossiers médicaux électroniques</term><term>Fouille de données</term><term>Humains</term><term>Traitement du langage naturel</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The widespread use of electronic health records (EHRs) has generated massive clinical data storage. Association rules mining is a feasible technique to convert this large amount of data into usable knowledge for clinical decision making, research or billing. We present a data driven method to create a knowledge base linking medications to pathological conditions through their therapeutic indications from elements within the EHRs.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">25888890</PMID><DateCreated><Year>2015</Year><Month>05</Month><Day>01</Day></DateCreated><DateCompleted><Year>2016</Year><Month>04</Month><Day>04</Day></DateCompleted><DateRevised><Year>2015</Year><Month>05</Month><Day>01</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1472-6947</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><PubDate><Year>2015</Year></PubDate></JournalIssue><Title>BMC medical informatics and decision making</Title><ISOAbbreviation>BMC Med Inform Decis Mak</ISOAbbreviation></Journal><ArticleTitle>Non-redundant association rules between diseases and medications: an automated method for knowledge base construction.</ArticleTitle><Pagination><MedlinePgn>29</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12911-015-0151-9</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The widespread use of electronic health records (EHRs) has generated massive clinical data storage. Association rules mining is a feasible technique to convert this large amount of data into usable knowledge for clinical decision making, research or billing. We present a data driven method to create a knowledge base linking medications to pathological conditions through their therapeutic indications from elements within the EHRs.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Association rules were created from the data of patients hospitalised between May 2012 and May 2013 in the department of Cardiology at the University Hospital of Strasbourg. Medications were extracted from the medication list, and the pathological conditions were extracted from the discharge summaries using a natural language processing tool. Association rules were generated along with different interestingness measures: chi square, lift, conviction, dependency, novelty and satisfaction. All medication-disease pairs were compared to the Summary of Product Characteristics, which is the gold standard. A score based on the other interestingness measures was created to filter the best rules, and the indices were calculated for the different interestingness measures.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">After the evaluation against the gold standard, a list of accurate association rules was successfully retrieved. Dependency represents the best recall (0.76). Our score exhibited higher exactness (0.84) and precision (0.27) than all of the others interestingness measures. Further reductions in noise produced by this method must be performed to improve the classification precision.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Association rules mining using the unstructured elements of the EHR is a feasible technique to identify clinically accurate associations between medications and pathological conditions.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Séverac</LastName><ForeName>François</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Laboratoire de Biostatistique et d'Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. francois.severac@chru-strasbourg.fr.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. francois.severac@chru-strasbourg.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sauleau</LastName><ForeName>Erik A</ForeName><Initials>EA</Initials><AffiliationInfo><Affiliation>Laboratoire de Biostatistique et d'Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. ea.sauleau@unistra.fr.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. ea.sauleau@unistra.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Meyer</LastName><ForeName>Nicolas</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Laboratoire de Biostatistique et d'Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. nmeyer@unistra.fr.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. nmeyer@unistra.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lefèvre</LastName><ForeName>Hassina</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. hassina.lefevre@chru-strasbourg.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nisand</LastName><ForeName>Gabriel</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France. gabriel.nisand@chru-strasbourg.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jay</LastName><ForeName>Nicolas</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>LORIA - Equipe Orpailleurs, Université de Nancy, Nancy, France. nicolas.jay@loria.fr.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>SPI-EAO, Faculté de Médecine, Université de Nancy, Nancy, France. nicolas.jay@loria.fr.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>04</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Med Inform Decis Mak</MedlineTA><NlmUniqueID>101088682</NlmUniqueID><ISSNLinking>1472-6947</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>J Gen Intern Med. 2005 Feb;20(2):143-7</RefSource><PMID Version="1">15836547</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Stud Health Technol Inform. 2006;124:845-50</RefSource><PMID Version="1">17108618</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Drug Saf. 2014 Oct;37(10):777-90</RefSource><PMID Version="1">25151493</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Hosp Med. 2007 Jul;2(4):199-202</RefSource><PMID Version="1">17683098</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Biomed Inform. 2010 Dec;43(6):891-901</RefSource><PMID Version="1">20884377</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>N Engl J Med. 1968 Mar 14;278(11):593-600</RefSource><PMID Version="1">5637758</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Am Med Inform Assoc. 2011 Nov-Dec;18(6):859-67</RefSource><PMID Version="1">21613643</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Am Med Inform Assoc. 2014 Mar-Apr;21(2):353-62</RefSource><PMID Version="1">24158091</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>AMIA Annu Symp Proc. 2006;:925</RefSource><PMID Version="1">17238544</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Am J Manag Care. 2002 Jan;8(1):37-43</RefSource><PMID Version="1">11814171</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Ann N Y Acad Sci. 1965 Aug 6;126(2):795-804</RefSource><PMID Version="1">5217245</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>JAMA. 2011 Aug 24;306(8):848-55</RefSource><PMID Version="1">21862746</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D002318">Cardiovascular Diseases</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000188">drug therapy</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D057225">Data Mining</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D057286">Electronic Health Records</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D051188">Knowledge Bases</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D009323">Natural Language Processing</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4415340</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>5</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>3</Month><Day>26</Day></PubMedPubDate><PubMedPubDate PubStatus="aheadofprint"><Year>2015</Year><Month>4</Month><Day>15</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>4</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>4</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>4</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1186/s12911-015-0151-9</ArticleId><ArticleId IdType="pii">10.1186/s12911-015-0151-9</ArticleId><ArticleId IdType="pubmed">25888890</ArticleId><ArticleId IdType="pmc">PMC4415340</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>France</li></country><region><li>Alsace (région administrative)</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li><li>Strasbourg</li></settlement><orgName><li>Nancy-Université</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Severac, Francois" sort="Severac, Francois" uniqKey="Severac F" first="François" last="Séverac">François Séverac</name></region><name sortKey="Jay, Nicolas" sort="Jay, Nicolas" uniqKey="Jay N" first="Nicolas" last="Jay">Nicolas Jay</name><name sortKey="Lefevre, Hassina" sort="Lefevre, Hassina" uniqKey="Lefevre H" first="Hassina" last="Lefèvre">Hassina Lefèvre</name><name sortKey="Meyer, Nicolas" sort="Meyer, Nicolas" uniqKey="Meyer N" first="Nicolas" last="Meyer">Nicolas Meyer</name><name sortKey="Nisand, Gabriel" sort="Nisand, Gabriel" uniqKey="Nisand G" first="Gabriel" last="Nisand">Gabriel Nisand</name><name sortKey="Sauleau, Erik A" sort="Sauleau, Erik A" uniqKey="Sauleau E" first="Erik A" last="Sauleau">Erik A. 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