A ranking-based approach for supporting the initial selection of primary studies in a Systematic Literature Review

Gonzalez-Toral, Santiago; Freire, Renán; Gualán, Ronald; Saquicela, Víctor

doi:10.1109/clei47609.2019.235079

Cited by 7 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For automated metadata extraction, we have found just one study. In this study, González-Toral, Freire, Gualán and Saquicela [40] describe a Python-based algorithm that automates metadata extraction from articles published by IEEE, ACM, Springer, Scopus, and Semantic Scholar. They extracted metadata through each repositories' REST API using a customized search query, consisting of Boolean operators, filters, and specific metadata.…”

Section: Which Databases Have Been Used For Automated Metadata Extraction?mentioning

confidence: 99%

“…They extracted metadata through each repositories' REST API using a customized search query, consisting of Boolean operators, filters, and specific metadata. Since ACM and Semantic Scholar do not have an API that suits this challenge, González-Toral, Freire, Gualán and Saquicela [40] developed a web-scraping algorithm using the Selenium Python library. The collected metadata M was stored in the following scheme: M = {title; abstract; keywords; year; authors; doi} Once the metadata was stored, the title, abstract, and keywords were processed using NLP and unsupervised machine learning techniques to identify which papers are the most relevant for citation screening.…”

Section: Which Databases Have Been Used For Automated Metadata Extraction?mentioning

confidence: 99%

“…For automated metadata extraction, we have found just one study. In this study, González-Toral, Freire, Gualán and Saquicela [40] describe an algorithm that automates metadata extraction from articles Cost assignment [66] Data resampling [41,42,58,64] SMOTE [43,53] Feature enrichment [43,63] Cost-sensitive classifiers [43,53,61,66] Precision@95% recall [ published by IEEE, ACM, Springer, Scopus, and Semantic Scholar. The fact that we did not find other articles mentioning metadata extraction of scientific literature might indicate that these domains are not explicitly described in the literature, or that we should have widened our search query.…”

Section: Rq3a Which Databases Have Been Used For Automated Metadata Extraction?mentioning

confidence: 99%

See 2 more Smart Citations

Automation of systematic literature reviews: A systematic literature review

Dinter

Teki̇nerdoğan

Çatal

2021

Information and Software Technology

188

View full text Add to dashboard Cite

Section: Which Databases Have Been Used For Automated Metadata Extraction?mentioning

confidence: 99%

Section: Which Databases Have Been Used For Automated Metadata Extraction?mentioning

confidence: 99%

Section: Rq3a Which Databases Have Been Used For Automated Metadata Extraction?mentioning

confidence: 99%

See 1 more Smart Citation

Automation of systematic literature reviews: A systematic literature review

Dinter

Teki̇nerdoğan

Çatal

2021

Information and Software Technology

188

View full text Add to dashboard Cite

“…Some meta-evidence similar data researches, such as the CISMeF metadata project, based on the Dublin core model, could describe the metadata of EBM resources [ 26 ], and Xu et al [ 27 ] established an evidence-based medicine metadata experiment database. Another study [ 28 ] reported a web scraping algorithm developed by python language that can automatically extract metadata of published literature (such as title, abstract, keywords, year, author, and DOI). A brief comparison of automatic SR (and/or meta-analysis) and classical manual SR is shown in Table 4 .…”

Section: Discussionmentioning

confidence: 99%

Construction of a Meta-Evidence Prototype Database of Traditional Chinese Medicine Splenogastric Diseases and Its Application in an Automatic Meta-Analysis System

Zhang

Wang

Yao

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

Background. Traditional Chinese medicine splenogastric diseases (TCMSDs) are equivalent to digestive system diseases in modern medicine. The forms of clinical evidence of TCMSDs include clinical trials, such as randomized controlled trials (RCTs) and systematic reviews (SRs). SRs mainly rely on manual operations and have the shortcomings of time consumption and low efficiency; therefore, they cannot meet the needs of rapid clinical decision-making. It is urgent to establish a new and smart form of a database to support the progress of SRs. Methods. We searched and screened all TCMSD RCT reports, in both Chinese and English, and extracted them into meta-evidence through predesigned structural Microsoft Excel tables. All meta-evidence was imported into an online clinical meta-evidence collection and management system after data quality checking. The meta-evidence database of traditional Chinese medicine (TCM) splenogastric disease (MED-TCMSD) was then tested as a backend of an automatic meta-analysis system. Results. A total of 405 cases of TCMSD RCTs were processed into meta-evidence. The most common diseases were stomach stuffiness disease, epigastralgia, and chronic atrophic gastritis. Banxiaxiexin decoction and its modifications were the most used interventions. More than half of the cases employed TCM in conjunction with regular therapeutics. The top reported outcomes included clinical effects, adverse events, and TCM syndromes. The MED-TCMSD worked well as a part of the automatic meta-analysis system. Conclusions. We developed and tested a new form of clinical evidence, meta-evidence, for automatic SR and fast evidence-based decision-making. As an example of the MED, the MED-TCMSD can improve the production and updating efficiency of the evidence of TCMSDs. The methods of constructing the MED-TCMSD can be further applied to the development of MEDs of other diseases.

show abstract

“…TC techniques addressing the use of Text Mining (TM), Natural Language Processing (NLP) and Machine Learning (ML) are strongly adopted by researchers to automate the selection of studies. The most adopted ML models with promising results involves supervised ML models such as Support Vector Machines (SVM) [17]- [19], [34] and/or active learning [17], [33], [36], [43]. Variations of the Naïve Bayes classifier have been also explored [32], [34], [37] as well as Hybrid Feature Selection Method (HFSM) combined with other algorithms such as the hierarchical low-rank decomposition Blocked Adaptive Cross Approximation (BACA) [30] and the classical Rules7 [31].…”

Section: A Rq1: What Are the Existing Approaches And Tools To Support...mentioning

confidence: 99%

Automated Support for Searching and Selecting Evidence in Software Engineering: A Cross-domain Systematic Mapping

Petrillo

Hallé

2021

2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

View full text Add to dashboard Cite

Searching and selecting relevant evidence is crucial to answer research questions from secondary studies in Software Engineering (SE). The activities of search and selection of studies are labour-intensive, time-consuming and demand automation support. Objective: Our goal is to identify and summarize the state-of-the-art on automation support for searching and selecting evidence for secondary studies in SE. Method: We performed a systematic mapping on existing automating support to search and select evidence for secondary studies in SE, expanding our investigation in a cross-domain study addressing advancements from the medical field. Results: Our results show that the SE field has a variety of tools and Text Classification (TC) approaches to automate the search and selection activities. However, medicine has more well-established tools with a larger adoption than SE. Cross-validation and experiment are the most adopted methods to assess TC approaches. Furthermore, recall and precision are the most adopted assessment metrics. Conclusion: Automated approaches for searching and selecting studies in SE have not been applied in practice by SE researchers. Integrated and easy-to-use automated approaches addressing consolidated TC techniques can bring relevant advantages on workload and time saving for SE researchers who conduct secondary studies.

show abstract

A ranking-based approach for supporting the initial selection of primary studies in a Systematic Literature Review

Cited by 7 publications

References 31 publications

Automation of systematic literature reviews: A systematic literature review

Automation of systematic literature reviews: A systematic literature review

Construction of a Meta-Evidence Prototype Database of Traditional Chinese Medicine Splenogastric Diseases and Its Application in an Automatic Meta-Analysis System

Automated Support for Searching and Selecting Evidence in Software Engineering: A Cross-domain Systematic Mapping

Contact Info

Product

Resources

About