Commentary on EPC methods: an exploration of the use of text-mining software in systematic reviews

Paynter, Robin; Bañez, Lionel L.; Erinoff, Eileen; Lege-Matsuura, Jennifer; Potter, Shannon

doi:10.1016/j.jclinepi.2016.11.019

Cited by 34 publications

(43 citation statements)

References 5 publications

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“…Jonnalagadda et al focused on automating data extraction, whereas the other review, O'Mara‐Eves et al, aimed at presenting research on using text mining for study identification . In April 2016, the Agency for Healthcare Research and Quality published a “research white paper” exploring the use of text mining tools, including screening tools, in the systematic review process …”

Section: Introductionmentioning

confidence: 99%

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Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan

Olofsson

Brolund

Hellberg

et al. 2017

Research Synthesis Methods

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Section: Introductionmentioning

confidence: 99%

“…Our evaluation is, to our knowledge, the first evaluation of Rayyan done by independent users. Paynter et al mentioned Rayyan in the Agency for Healthcare Research and Quality research white paper, but no evaluation was done …”

Section: Introductionmentioning

confidence: 99%

Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan

Olofsson

Brolund

Hellberg

et al. 2017

Research Synthesis Methods

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show abstract

“…Ultimately, they categorised documents according to these annotations. Thus, combining text mining methods for systematic review is a hot topic [72][73][74][75].…”

Section: Methods Analysismentioning

confidence: 99%

Patient healthcare trajectory. An essential monitoring tool: a systematic review

Pinaire

Azé

Bringay

et al. 2017

Health Inf Sci Syst

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Background: Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. However, the rationale for studying patients' trajectories, and how this trajectory concept is defined remains a public health challenge. Our research was focused on patients' trajectories based on disease management and care, while also considering medico-economic aspects of the associated management. We illustrated this concept with an example: a myocardial infarction (MI) occurring in a patient's hospital trajectory of care. The patient follow-up was traced via the prospective payment system. We applied a semi-automatic text mining process to conduct a comprehensive review of patient healthcare trajectory studies. This review investigated how the concept of trajectory is defined, studied and what it achieves.Methods: We performed a PubMed search to identify reports that had been published in peer-reviewed journals between January 1, 2000 and October 31, 2015. Fourteen search questions were formulated to guide our review. A semi-automatic text mining process based on a semantic approach was performed to conduct a comprehensive review of patient healthcare trajectory studies. Text mining techniques were used to explore the corpus in a semantic perspective in order to answer non-a priori questions. Complementary review methods on a selected subset were used to answer a priori questions.Results: Among the 33,514 publications initially selected for analysis, only 70 relevant articles were semi-automatically extracted and thoroughly analysed. Oncology is particularly prevalent due to its already well-established processes of care. For the trajectory thema, 80% of articles were distributed in 11 clusters. These clusters contain distinct semantic information, for example health outcomes (29%), care process (26%) and administrative and financial aspects (16%). Conclusion:This literature review highlights the recent interest in the trajectory concept. The approach is also gradually being used to monitor trajectories of care for chronic diseases such as diabetes, organ failure or coronary artery and MI trajectory of care, to improve care and reduce costs. Patient trajectory is undoubtedly an essential approach to be further explored in order to improve healthcare monitoring.

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“…Paynter et al undertook an overview of text mining tools and techniques in systematic reviews and identified 111 tools, of which 52 support searching. They concluded that “although it seems promising, text mining has not become a standard tool for creating systematic review search strategies” (p. 13) and noted that one possible limitation was that many tools have been developed based upon output from PubMed or Medline.…”

Section: Background and Aimsmentioning

confidence: 99%

“…There seems to be a paucity of published literature on text‐mining procedures for identifying free‐text and controlled terms for specific databases using generic tools, although EUnetHTA and Gourlay provide some guidance on obtaining term frequencies. Controlled vocabularies can also be analysed using database specific tools, particularly for Medline and PubMed, and these are listed elsewhere , . However, there is little guidance on using the variety of text mining tools available to complement other methods to identify search terms for undertaking systematic reviews.…”

Section: Background and Aimsmentioning

confidence: 99%

Text mining for search term development in systematic reviewing: A discussion of some methods and challenges

Stansfield

O’Mara-Eves

Thomas

2017

Research Synthesis Methods

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Using text mining to aid the development of database search strings for topics described by diverse terminology has potential benefits for systematic reviews; however, methods and tools for accomplishing this are poorly covered in the research methods literature. We briefly review the literature on applications of text mining for search term development for systematic reviewing. We found that the tools can be used in five overarching ways: improving the precision of searches; identifying search terms to improve search sensitivity; aiding the translation of search strategies across databases; searching and screening within an integrated system; and developing objectively-derived search strategies. Using a case study and selected examples, we then reflect on the utility of certain technologies (TF-IDF and Termine, term frequency, and clustering) in improving the precision and sensitivity of searches. Challenges in using these tools are discussed. The utility of these tools is influenced by the different capabilities of the tools, the way the tools are used, and the text that is analysed. Increased awareness of how the tools perform facilitates the further development of methods for their use in systematic reviews.

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Commentary on EPC methods: an exploration of the use of text-mining software in systematic reviews

Cited by 34 publications

References 5 publications

Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan

Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan

Patient healthcare trajectory. An essential monitoring tool: a systematic review

Text mining for search term development in systematic reviewing: A discussion of some methods and challenges

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