Global research on keratomycosis: New insights from latent Dirichlet allocation and HJ-Biplot-driven knowledge mapping study.

BACKGROUND: Keratomycosis is a form of infectious keratitis, an infection of the cornea, which is caused by fungi. This disease is a leading cause of ocular morbidity globally with at least 60 % of the affected individuals becoming monocularly blind. OBJECTIVE: This bibliometric analysis aimed to comprehensively assess the existing body of literature, providing insights of the evolution of keratomycosis research by identifying key themes and research gaps. METHODS: This work used the modeling method Latent Dirichlet Allocation (LDA) to identify and interpret scientific information on topics concerning existing categories in a set of documents. The HJ-Biplot method was also used to determine the relationship between the analyzed topics, taking into consideration the years under study. RESULTS: This bibliometric analysis was performed on a total of 2,599 scientific articles published between 1992 and 2022. The five leading countries with more scientific production and citations on keratomycosis were The United States of America, followed by India, China, United Kingdom and Australia. The top five topics studied were Case Reports and Corneal Infections, which exhibited a decreasing trend; followed by Penetrating Keratoplasty and Corneal Surgery, Ocular Effects of Antifungal Drugs, Gene Expression and Inflammatory Response in the Cornea and Patient Data which have been increasing throughout the years. However Filamentous Fungi and Specific Pathogens, and Antifungal Therapies research has been decreasing in trend. CONCLUSION: Additional investigation into innovative antifungal drug therapies is crucial for proactively tackling the potential future resistance to antifungal agents in scientific writing.

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

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 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. 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.

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method.

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates of uncertainty. We illustrate our method using tree data for the eastern United States and from a tropical successional chronosequence. The model is able to detect pervasive declines in the oak community in Minnesota and Indiana, potentially due to fire suppression, increased growing season precipitation and herbivory. The chronosequence analysis is able to delineate clear successional trends in species composition, while also revealing that site-specific factors significantly impact these successional trajectories. The proposed method provides a means to decompose and track the dynamics of species assemblages along temporal and spatial gradients, including effects of global change and forest disturbances.