Extractive text summarization system to aid data extraction from full text in systematic review development

Bui, Duy Duc An; Fiol, Guilherme Del; Hurdle, John F.; Jonnalagadda, Siddhartha

doi:10.1016/j.jbi.2016.10.014

Cited by 55 publications

(28 citation statements)

References 33 publications

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“…Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [47,48]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [46,49].…”

Section: Some Frequently Asked Questions About Methodological Studiesmentioning

confidence: 99%

A tutorial on methodological studies: the what, when, how and why

Mbuagbaw

Lawson

Puljak

et al. 2020

BMC Med Res Methodol

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Background Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste. Main body We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies? Conclusion Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

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Section: Some Frequently Asked Questions About Methodological Studiesmentioning

confidence: 99%

A tutorial on methodological studies: the what, when, how and why

Mbuagbaw

Lawson

Puljak

et al. 2020

BMC Med Res Methodol

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“…A potential problem that clinical user face is the management of the results set to identify, appraise, and synthesize the best available evidence to answer the clinical question in the best possible manner. In all this process, a lot of manual effort is required to extract the data to make a summary, and it is also subjected to error [27]. Moreover, the context of the user may change the default ranking of an article to bring it to the top or take it to the bottom.…”

Section: Methodsmentioning

confidence: 99%

Impact of Automatic Query Generation and Quality Recognition Using Deep Learning to Curate Evidence From Biomedical Literature: Empirical Study

Afzal¹,

Hussain²,

Malik³

et al. 2019

JMIR Med Inform

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BackgroundThe quality of health care is continuously improving and is expected to improve further because of the advancement of machine learning and knowledge-based techniques along with innovation and availability of wearable sensors. With these advancements, health care professionals are now becoming more interested and involved in seeking scientific research evidence from external sources for decision making relevant to medical diagnosis, treatments, and prognosis. Not much work has been done to develop methods for unobtrusive and seamless curation of data from the biomedical literature.ObjectiveThis study aimed to design a framework that can enable bringing quality publications intelligently to the users’ desk to assist medical practitioners in answering clinical questions and fulfilling their informational needs.MethodsThe proposed framework consists of methods for efficient biomedical literature curation, including the automatic construction of a well-built question, the recognition of evidence quality by proposing extended quality recognition model (E-QRM), and the ranking and summarization of the extracted evidence.ResultsUnlike previous works, the proposed framework systematically integrates the echelons of biomedical literature curation by including methods for searching queries, content quality assessments, and ranking and summarization. Using an ensemble approach, our high-impact classifier E-QRM obtained significantly improved accuracy than the existing quality recognition model (1723/1894, 90.97% vs 1462/1894, 77.21%).ConclusionsOur proposed methods and evaluation demonstrate the validity and rigorousness of the results, which can be used in different applications, including evidence-based medicine, precision medicine, and medical education.

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“…At this point, the system is restricted to biomedical research papers only, but the authors intend to extend it to other types of SRs. A similar approach is followed by Bui et al (2016) The present document has been produced and adopted by the bodies identified above as author. This task has been carried out exclusively by the author in the context of a contract between the European Food Safety Authority and the author, awarded following a tender procedure.…”

Section: Classification Methodsmentioning

confidence: 99%

“…A more general information extraction algorithm was discussed by Basu et al (2016) who employ natural language processing (NLP) and machine learning to build information extraction algorithms to identify data elements in new publications without having to go through manual annotation to build golden standards for each data type. A similar approach was provided by Bui et al (2016). More information on these methods is provided in Section 2.2.2.…”

Section: Critical Appraisal and Synthesis Stage Of A Systematic Reviewmentioning

confidence: 99%

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Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

Jaspers

Troyer

Aerts

2018

EFS3

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This Report presents the results from EFSA project RC/EFSA/AMU/2016/01 related to the implementation of machine learning techniques for literature reviews and systematic reviews in EFSA. An overview of the different steps of a systematic review is provided, along with possible ways for automation. Although it was found that most steps could benefit from automation, it was also observed that some steps require more sophisticated methods than those encompassed within the machine learning framework. Availability of data and methodology allowed for the development of an automatic screening tool based on several machine learning techniques. The developed shiny R application can be used for the screening of abstracts and full texts. Properties of machine learning techniques are discussed in this Report together with their most important advantages and disadvantages. The latter discussion includes both general properties, as well as context-specific properties based on their performance in three case studies. Although creating a universal automatic data extraction tool was considered to be infeasible in this stage, this step of the systematic review was addressed to allow the reviewer to scan the uploaded pdf files for certain words or string of words. Based on observations from the performed case studies, recommendations were made regarding which methods are preferred in specific situations. More explicitly, a discussion is made about the performance of the classifiers with respect to the magnitude of the pool of papers to be screened as well as to the amount of imbalance, referring to the proportion of relevant and irrelevant papers. Finally, it was concluded that the results presented in this report provide proof that the developed shiny application could be efficiently used in combination with other software such as DistillerSR. © European Food Safety Authority, 2018Key words: Systematic Reviews, Machine Learning, screening, data extraction, Sensitivity, Specificity Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Reproduction is authorised provided the source is acknowledged. Machine Learning Techniques for Literature and Systematic Reviewswww.efsa.europa.eu/publications 3 EFSA Supporting publication 2018:EN-1427The present document has been produced and adopted by the bodies identified above as author. This task has been carried out exclusively by the author in the context of a contract ...

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Extractive text summarization system to aid data extraction from full text in systematic review development

Cited by 55 publications

References 33 publications

A tutorial on methodological studies: the what, when, how and why

A tutorial on methodological studies: the what, when, how and why

Impact of Automatic Query Generation and Quality Recognition Using Deep Learning to Curate Evidence From Biomedical Literature: Empirical Study

Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA

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