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Automatic learning framework for pharmaceutical record matching

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)es
dc.contributor.authorLópez Cuadrado, José Luis
dc.contributor.authorGonzález Carrasco, Israel
dc.contributor.authorLópez Hernández, Jesús Leonardo
dc.contributor.authorMartínez Fernández, Paloma
dc.contributor.authorMartínez Fernández, José Luis
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-11-10T12:40:21Z
dc.date.available2021-11-10T12:40:21Z
dc.date.issued2020-09-18
dc.description.abstractPharmaceutical manufacturers need to analyse a vast number of products in their daily activities. Many times, the same product can be registered several times by different systems using different attributes, and these companies require accurate and quality information regarding their products since these products are drugs. The central hypothesis of this research work is that machine learning can be applied to this domain to efficiently merge different data sources and match the records related to the same product. No human is able to do this in a reasonable way because the number of records to be matched is extremely high. This article presents a framework for pharmaceutical record matching based on machine learning techniques in a big data environment. The proposed framework aims to explode the well-known rules for the matching of records from different databases for training machine learning models. Then the trained models are evaluated by predicting matches with records that do not follow these known rules. Finally, the production environment is simulated by generating a huge amount of combinations of records and predicting the matches. The obtained results show that, despite the good results obtained with the training datasets, in the production environment, the average accuracy of the best model is around 85%. That shows that matches which do not follow the known rules can be predicted and, considering that there is not a human way to process this amount of data, the results are promising.en
dc.description.sponsorshipThis work was supported by the Research Program of the Ministry of Economy and competitiveness, Government of Spain, through the DeepEMR Project, under Grant TIN2017-87548-C2-1-Ren
dc.identifier.bibliographicCitationJ. L. López-Cuadrado, I. González-Carrasco, J. Leonardo López-Hernández, P. Martínez-Fernández and J. L. Martínez-Fernández, "Automatic Learning Framework for Pharmaceutical Record Matching," in IEEE Access, vol. 8, pp. 171754-171770, 2020, doi: 10.1109/ACCESS.2020.3024558en
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2020.3024558
dc.identifier.issn2169-3536
dc.identifier.publicationfirstpage171754
dc.identifier.publicationlastpage171770
dc.identifier.publicationtitleIEEE Accessen
dc.identifier.publicationvolume8
dc.identifier.urihttps://hdl.handle.net/10016/33564
dc.identifier.uxxiAR/0000026086
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. TIN2017-87548-C2-1-Res
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformáticaes
dc.subject.otherbig dataen
dc.subject.otherdata integrationen
dc.subject.othermachine learningen
dc.subject.otherpattern detectionen
dc.subject.othermedicineen
dc.titleAutomatic learning framework for pharmaceutical record matchingen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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