dc.contributor.author | Wolff, Patricio | |
dc.contributor.author | Rios, Sebastian | |
dc.contributor.author | Clavijo, David | |
dc.contributor.author | Graña Romay, Manuel María | |
dc.contributor.author | Carrasco, Miguel | |
dc.date.accessioned | 2021-02-19T10:52:44Z | |
dc.date.available | 2021-02-19T10:52:44Z | |
dc.date.issued | 2020-09-29 | |
dc.identifier.citation | Journal Of Biomedical Semantics 11(1) : (2020) // Article ID 12 | es_ES |
dc.identifier.issn | 2041-1480 | |
dc.identifier.uri | http://hdl.handle.net/10810/50216 | |
dc.description.abstract | Background Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, structure, and analyze in the shortest time possible the knowledge encoded in the large volume of medical literature. In this paper, we use the Latent Dirichlet Allocation approach to analyze the correlation between funding efforts and actually published research results in order to provide the policy makers with a systematic and rigorous tool to assess the efficiency of funding programs in the medical area. Results We have tested our methodology in the Revista Medica de Chile, years 2012-2015. 50 relevant semantic topics were identified within 643 medical scientific research papers. Relationships between the identified semantic topics were uncovered using visualization methods. We have also been able to analyze the funding patterns of scientific research underlying these publications. We found that only 29% of the publications declare funding sources, and we identified five topic clusters that concentrate 86% of the declared funds. Conclusions Our methodology allows analyzing and interpreting the current state of medical research at a national level. The funding source analysis may be useful at the policy making level in order to assess the impact of actual funding policies, and to design new policies. | es_ES |
dc.description.sponsorship | This research was partially funded by CONICYT, Programa de Formacion de Capital Humano avanzado (CONICYT-PCHA/Doctorado Nacional/2015-21150115). MG work in this paper has been partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and projects KK-2018/00071 and KK2018/00082 of the Elkartek 2018 funding program. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720. No role has been played by funding bodies in the design of the study and collection, analysis, or interpretation of data or in writing the manuscript. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | BioMed Central | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/777720 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/TIN2017-85827-P | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | data science | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | latent dirichlet allocation | es_ES |
dc.subject | healthcare management | es_ES |
dc.subject | strategy | es_ES |
dc.title | Methodologically Grounded SemanticAnalysis of Large Volume of Chilean Medical Literature Data Applied to the Analysis of Medical Research Funding Efficiency in Chile | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-020-00226-w | es_ES |
dc.identifier.doi | 10.1186/s13326-020-00226-w | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |