Constructing and evaluating automated literature review systems

Portenoy, Jason; West, Jevin D.

doi:10.1007/s11192-020-03490-w

Cited by 12 publications

(12 citation statements)

References 29 publications

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“…The number and quality of searching result of apps depend heavily on keywords while limited keywords may lead to a biased analysis [ 31 ]. To obtain a comprehensive, objective, and unbiased keyword list for drug refence app, we employed a method based on the snowballing technique that was widely used in literature search tasks in conducting systematic reviews [ 32 , 33 ]. The snowballing method may increase the yield of search results by 2.5–43% [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Personalized and Self-Management: Systematic Search and Evaluation Quality Factors and User Preference of Drug Reference Apps in Taiwan

Chen

Liao

et al. 2021

JPM

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Background: Drug reference apps promote self-management and improve the efficiency and quality of work for physicians, nurses, pharmacists, and patients. This study aimed to describe a systematic and stepwise process to identify drug reference apps in Taiwan, assess the quality of these apps, and analyze the influential factors for user ratings. Methods: A two-step algorithm (KESS) consisting of keyword growing and systematic search was proposed. Seven independent reviewers were trained to evaluate these apps using Mobile App Rating Scale (MARS). A logistic regression model was fitted and average marginal effects (AME) were calculated to identify the effects of factors for higher user ratings. Results: A total of 23 drug reference apps in Taiwan were identified and analyzed. Generally, these drug reference apps were evaluated as acceptable quality with an average MARS score of 3.23. Higher user engagement, more functionality, better aesthetics, and more information associated with higher user ratings. Navigation is the most influential factor on higher user ratings (AME: 13.15%) followed by performance (AME: 11.03%), visual appeal (AME: 10.87%), credibility (AME: 10.67%), and quantity of information (AME: 10.42%). Conclusions: User experience and information clearly affect user ratings of drug reference apps. Five key factors should be considered when designing drug reference apps.

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Section: Methodsmentioning

confidence: 99%

Personalized and Self-Management: Systematic Search and Evaluation Quality Factors and User Preference of Drug Reference Apps in Taiwan

Chen

Liao

et al. 2021

JPM

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“…Scientific Recommendations. Work in this area typically focuses on recommending papers, using proxies such as citations or coauthorship links in place of ground truth [2,30,36], or a combination of text and citation data [7]. In addition to being noisy proxies in terms of relevance, these signals reinforce existing patterns of citation or collaboration, and are not indicative of papers or authors that would help users generate novel research directions -the focus of Bridger.…”

Section: Related Workmentioning

confidence: 99%

Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

Portenoy¹,

Radensky²,

West³

et al. 2021

Preprint

Self Cite

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Scientific silos can hinder innovation. These information "filter bubbles" and the growing challenge of information overload limit awareness across the literature, making it difficult to keep track of even narrow areas of interest, let alone discover new ones. Algorithmic curation and recommendation, which often prioritize relevance, can further reinforce these bubbles. In response, we describe Bridger, a system for facilitating discovery of scholars and their work, to explore design tradeoffs among relevant and novel recommendations. We construct a faceted representation of authors using information extracted from their papers and inferred personas. We explore approaches both for recommending new content and for displaying it in a manner that helps researchers to understand the work of authors who they are unfamiliar with. In studies with computer science researchers, our approach substantially improves users' abilities to do so. We develop an approach that locates commonalities and contrasts between scientists-retrieving partially similar authors, rather than aiming for strict similarity. We find this approach helps users discover authors useful for generating novel research ideas of relevance to their work, at a higher rate than a state-of-art neural model. Our analysis reveals that Bridger connects authors who have different citation profiles, publish in different venues, and are more distant in social co-authorship networks, raising the prospect of bridging diverse communities and facilitating discovery.

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“…Several existing systems focus on automating parts of the systematic review process more broadly [ 90 ]. These systems focus on supporting the identification of relevant studies [ 5 , 64 , 70 , 71 , 94 ] or extracting PICO elements [ 22 , 42 , 55 , 62 ]. The recently released Trialstreamer system allows users to discover new clinical trials using PICO-based search [ 61 ].…”

Section: Systematic Review Automationmentioning

confidence: 99%

Text mining approaches for dealing with the rapidly expanding literature on COVID-19

Wang

2020

Briefings in Bioinformatics

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More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system’s performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.

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Constructing and evaluating automated literature review systems

Cited by 12 publications

References 29 publications

Personalized and Self-Management: Systematic Search and Evaluation Quality Factors and User Preference of Drug Reference Apps in Taiwan

Personalized and Self-Management: Systematic Search and Evaluation Quality Factors and User Preference of Drug Reference Apps in Taiwan

Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

Text mining approaches for dealing with the rapidly expanding literature on COVID-19

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