Non-redundant association rules between diseases and medications: an automated method for knowledge base construction
Identifieur interne : 000753 ( Main/Exploration ); précédent : 000752; suivant : 000754Non-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.
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.
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.
Association rules mining using the unstructured elements of the EHR is a feasible technique to identify clinically accurate associations between medications and pathological conditions.
Url:
DOI: 10.1186/s12911-015-0151-9
PubMed: 25888890
PubMed Central: 4415340
Affiliations:
- France
- Alsace (région administrative), Grand Est, Lorraine (région)
- Nancy, Strasbourg
- Nancy-Université
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Le document en format XML
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Sauleau</name><affiliation wicri:level="3"><nlm:aff id="Aff1">Laboratoire de Biostatistique et d’Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg, France</nlm:aff><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><affiliation wicri:level="3"><nlm:aff id="Aff2">Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France</nlm:aff><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 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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:aff id="Aff2">Groupe Méthode en Recherche Clinique, Service de Santé Publique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France</nlm:aff><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:aff id="Aff3">LORIA – Equipe Orpailleurs, Université de Nancy, Nancy, France</nlm:aff><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><affiliation wicri:level="4"><nlm:aff id="Aff4">SPI-EAO, Faculté de Médecine, Université de Nancy, Nancy, France</nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>SPI-EAO, Faculté de Médecine, 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">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"><sec><title>Background</title><p>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.</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p>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.</p></sec><sec><title>Conclusions</title><p>Association rules mining using the unstructured elements of the EHR is a feasible technique to identify clinically accurate associations between medications and pathological conditions.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Weed, Ll" uniqKey="Weed L">LL Weed</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hartung, Dm" uniqKey="Hartung D">DM Hartung</name></author><author><name sortKey="Hunt, J" uniqKey="Hunt J">J Hunt</name></author><author><name sortKey="Siemienczuk, J" uniqKey="Siemienczuk J">J Siemienczuk</name></author><author><name sortKey="Miller, H" uniqKey="Miller H">H Miller</name></author><author><name sortKey="Touchette, Dr" uniqKey="Touchette D">DR 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Maloney</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Srikant, R" uniqKey="Srikant R">R Srikant</name></author><author><name sortKey="Agrawal, R" uniqKey="Agrawal R">R Agrawal</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Baralis, E" uniqKey="Baralis E">E Baralis</name></author><author><name sortKey="Cagliero, L" uniqKey="Cagliero L">L Cagliero</name></author><author><name sortKey="Cerquitelli, T" uniqKey="Cerquitelli T">T Cerquitelli</name></author><author><name sortKey="Garza, P" uniqKey="Garza P">P Garza</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><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="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="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. Sauleau</name><name sortKey="Sauleau, Erik A" sort="Sauleau, Erik A" uniqKey="Sauleau E" first="Erik A" last="Sauleau">Erik A. Sauleau</name><name sortKey="Severac, Francois" sort="Severac, Francois" uniqKey="Severac F" first="François" last="Séverac">François Séverac</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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