Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile

Journal of Biomedical Semantics

Patricio Wolff¹  Sebastián Ríos¹  David Clavijo¹  Manuel Graña²  Miguel Carrasco³ 

[1] Business Intelligence Research Center, Universidad de Chile, Beauchef 851, Santiago, 8370459, Santiago, Chile;Computational Intelligence Group, University of Basque Country, P. Manuel Lardizabal 1, 20018, San Sebastián, Spain;Universidad Adolfo Ibañez, Santiago, Chile;

关键词: Data science;    Machine learning;    Latent Dirichlet allocation;    Healthcare management;    Strategy;   

DOI  :  10.1186/s13326-020-00226-w

来源: Springer

【 摘 要 】

BackgroundMedical 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.ResultsWe have tested our methodology in the Revista Médica 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.ConclusionsOur 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.

【 授权许可】

CC BY