A large scale study of SVM based methods for abstract screening in systematic reviews

  title={A large scale study of SVM based methods for abstract screening in systematic reviews},
  author={T. K. Saha and Mourad Ouzzani and Hossam M. Hammady and Ahmed K. Elmagarmid},
A major task in systematic reviews is abstract screening, i.e., excluding, often hundreds or thousand of, irrelevant citations returned from a database search based on titles and abstracts. Thus, a systematic review platform that can automate the abstract screening process is of huge importance. Several methods have been proposed for this task. However, it is very hard to clearly understand the applicability of these methods in a systematic review platform because of the following challenges… 

Figures and Tables from this paper

Machine learning techniques for the automation of literature reviews and systematic reviews in <fc>EFSA</fc>

It was concluded that the results presented in this report provide proof that the developed shiny R application could be efficiently used in combination with other software such as DistillerSR.

Best practice guidelines for abstract screening large‐evidence systematic reviews and meta‐analyses

10 proposed guidelines to provide a practical set of best practice guidelines to help future review teams and managers are explained using real‐world examples or illustrations from applications.

Effect of Combination of HBM and Certainty Sampling on Workload of Semi-Automated Grey Literature Screening

Evaluations over three real-world grey literature datasets demonstrate that the proposed semi-automated grey literature screening approach can save up to 64.88% of the human screening workload, while maintaining high screening accuracy.

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

The results show that the SE field has a variety of tools and Text Classification approaches to automate the search and selection activities, however, medicine has more well-established tools with a larger adoption than SE.



Supporting systematic reviews using LDA-based document representations

A topic-based feature representation of documents outperforms the BOW representation when applied to the task of automatic citation screening and proposed term-enriched topics are more informative and less ambiguous to systematic reviewers.

Learning to identify relevant studies for systematic reviews using random forest and external information

This work introduces a novel method for representing systematic reviews based not only on lexical features, but also utilizing word clustering and citation features that is shown to outperform previously used features in representing systematic Reviews, regardless of the classifier.

Using text mining for study identification in systematic reviews: a systematic review of current approaches

Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in ‘live’ reviews, and the use of text mining as a ‘second screener’ may also be used cautiously.

Machine Learning Methods in Systematic Reviews: Identifying Quality Improvement Intervention Evaluations

A semiautomated literature screening process applied to the title and abstract screening stage of systematic reviews of complex, steadily expanding research fields such as quality improvement appeared to be associated with improved predictive performance.

Research Paper: Reducing Workload in Systematic Review Preparation Using Automated Citation Classification

Whether automated classification of document citations can be useful in reducing the time spent by experts reviewing journal articles for inclusion in updating systematic reviews of drug class efficacy for treatment of disease is investigated.

Studying the potential impact of automated document classification on scheduling a systematic review update

An initial analysis of the opportunities and challenges in aiding the SR update planning process with an informatics-based machine learning approach is performed, finding alerts could be a useful tool in the planning, scheduling, and allocation of resources for SR updates, providing an improvement in timeliness and coverage for the large number of medical topics needing SRs.

Active learning for biomedical citation screening

This work proposes a novel active learning strategy that exploits a priori domain knowledge provided by the expert (specifically, labeled features) and extends this model via a Linear Programming algorithm for situations where the expert can provide ranked labeled features.

Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision

A novel method is proposed that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS and it is shown that this approach tends to outperform existing methods with respect to automated PICO extraction.

A Prospective Evaluation of an Automated Classification System to Support Evidence-based Medicine and Systematic Review.

A prospective study of a support vector machine-based classifier for supporting the SR literature triage process demonstrates that these methods can achieve accurate results in near-real world conditions and are promising tools for deployment to groups conducting SRs.

Optimizing Text Quantifiers for Multivariate Loss Functions

This article addresses the problem of quantification by using a supervised learning model for structured prediction, capable of generating classifiers directly optimized for the (multivariate and nonlinear) function used for evaluating quantification accuracy.