Automating Biomedical Evidence Synthesis: RobotReviewer

Marshall, Iain J.; Kuiper, Joël; Banner, Edward A.; Wallace, Byron C.

doi:10.18653/v1/p17-4002

Cited by 61 publications

(45 citation statements)

References 20 publications

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“…Such models would further aid search, and might eventually facilitate automated knowledge-base construction for the clinical trials literature. Furthermore, automatic extraction of structured data would enable automation of the manual evidence synthesis process (Marshall et al, 2017). …”

Section: Tasks and Baselinesmentioning

confidence: 99%

“…Prior work has explored the use of NLP methods to automate biomedical evidence extraction and synthesis (Boudin et al, 2010; Marshall et al, 2017; Ferracane et al, 2016; Verbeke et al, 2012). 1 But the area has attracted less attention than it might from the NLP community, due primarily to a dearth of publicly available, annotated corpora with which to train and evaluate models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

Nye¹,

Li²,

Patel³

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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155

207

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We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the ‘PICO’ elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.

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Section: Tasks and Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

Nye¹,

Li²,

Patel³

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Self Cite

155

207

View full text Add to dashboard Cite

show abstract

“…Yet despite advances, extraction technologies remain in formative stages and are not readily accessible by practitioners. For systematic reviews of RCTs, there exist only a few prototype platforms that make such technologies available (ExaCT [33] and RobotReviewer [12,34,35] being among these). For systematic reviews in the basic sciences, the UK National Centre for Text Mining (NaCTeM) has created a number of systems which use structured models to automatically extract concepts including genes and proteins, yeasts, and anatomical entities [36], amongst other ML-based text mining tools.…”

Section: Data Extractionmentioning

confidence: 99%

Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

2019

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Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice.

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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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Automating Biomedical Evidence Synthesis: RobotReviewer

Cited by 61 publications

References 20 publications

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

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

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