Use of Medical Subject Headings (MeSH) in Portuguese for categorizing web-based healthcare content

Mancini, Félipe; Sousa, Fernando Sequeira; Teixeira, Fábio Oliveira; Falcão, Alex Esteves Jaccoud; Hummel, Anderson Diniz; Costa, Thiago Martini da; Calado, Pável; Araújo, Luciano Vieira de; Pisa, Ivan Torres

doi:10.1016/j.jbi.2010.12.002

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…Furthermore, some participants reported that those in charge of content provision lack familiarity with MeSH C . Although these two issues are quite common when using MeSH; this has proven to be adequate in several circumstances (18)(19)(20), and the provision of support and training usually helps people to better perform in indexing information (21). Nevertheless, further investigation would be needed to evaluate the MeSH C implementation into each tool and the support provided to encoders.…”

Section: Discussionmentioning

confidence: 99%

EUnetHTA INFORMATION MANAGEMENT SYSTEM: DEVELOPMENT AND LESSONS LEARNED

Chalon¹,

Kraemer²

2014

Int J Technol Assess Health Care

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While being entitled a project on IT infrastructure, the work program was also about people. When having to agree on complex topics, fostering a cohesive group dynamic and hosting face to face meetings brings added value and enhances understanding between partners. The adoption of widespread standards enhanced the homogeneity of the EUnetHTA tools and should thus contribute to their wider use, therefore, to the general objective of EUnetHTA. The initiatives on interoperability of systems need to be developed further to support a general interoperable information system that could benefit the whole HTA community.

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Section: Discussionmentioning

confidence: 99%

EUnetHTA INFORMATION MANAGEMENT SYSTEM: DEVELOPMENT AND LESSONS LEARNED

Chalon¹,

Kraemer²

2014

Int J Technol Assess Health Care

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“…The p-values belonging to each dataset were computed to determine whether there is any meaningful difference between categories (i.e., positive, negative) in datasets (i.e., Dataset1, Dataset2) [34]. Mann-Whitney non-parametric t-test p-values of Dataset1 and Dataset2 are 0.0012 and 0.0011, respectively.…”

Section: Sentiment Analysis With Topic Extraction Using Latent Dirich...mentioning

confidence: 99%

BERT-based Transfer Learning Model for COVID-19 Sentiment Analysis on Turkish Instagram Comments

Karayiğit

Akdağlı

Acı

2022

ITC

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First seen in Wuhan, China, the coronavirus disease (COVID-19) became a worldwide epidemic. Turkey’s first reported case was announced on March 11, 2020—the day the World Health Organization declared COVID-19 is a pandemic. Due to the intense and widespread use of social media during the pandemic, determining the role and effect (i.e., positive, negative, neutral) of social media gives us important information about society's perspective on events. In our study, two datasets (i.e. Dataset1, Dataset2) consisting of Instagram comments on COVID-19 were composed between different dates of the pandemic, and the change between users' feelings and thoughts about the epidemic was analyzed. The datasets are the first publicly available Turkish datasets on the sentiment analysis of COVID-19, as far as we know. The sentiment analysis of Turkish Instagram comments was performed using Machine Learning models (i.e., Traditional Machine Learning, Deep Learning, and BERT-based Transfer Learning). In the experiments, the balanced versions of these datasets (i.e. resDataset1, resDataset2) were taken into account as well as the original ones. The BERT-based Transfer Learning model achieved the highest classification success with 0.7864 macro-averaged F1 score values in resDataset1 and 0.7120 in resDataset2. It has been proven that the use of a pre-trained language model in Turkish datasets is more successful than other models in terms of classification performance.

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“…They evaluate a sampling technique described by Robnik-Šikonja and Kononenko [184] and compare it with further attribute selection techniques using standard data sets. The attribute selection techniques are benchmarked [11, 16, 19, 37, 43, 49, 50, 58, 73, 85, 88-90, 97, 99, 110, 118, 119, 121, 122, 124, 127, 129, 132, 133, 155, 169, 171, 209, 215, 219, 220, 223, 244] Bayesian networks [6,16,66,75,88,122,126,133,139,150,153,210,211,246] Combined classification [6,15,28,37,39,43,52,66,74,88,89,94,97,100,109,148,153,158,171,213,220,223,226] Decision rules [6,25,52,56,61,66,91,…”

Section: Classification Techniquesmentioning

confidence: 99%

Optimizing Hospital-wide Patient Scheduling

Gärtner¹

2014

Lecture Notes in Economics and Mathematical Systems

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Use of Medical Subject Headings (MeSH) in Portuguese for categorizing web-based healthcare content

Cited by 7 publications

References 51 publications

EUnetHTA INFORMATION MANAGEMENT SYSTEM: DEVELOPMENT AND LESSONS LEARNED

EUnetHTA INFORMATION MANAGEMENT SYSTEM: DEVELOPMENT AND LESSONS LEARNED

BERT-based Transfer Learning Model for COVID-19 Sentiment Analysis on Turkish Instagram Comments

Optimizing Hospital-wide Patient Scheduling

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