The Information Engineering Lab IELab @ UQ | The University of Queensland

About this project



This project seeks to develop methods and tools for improving how experts search for literature for systematic reviews. So far, research in this area by this team has provided useful tools for visually assisting Boolean query formulation, fully automatic methods for Boolean query refinement specifically in the systematic review domain, domain-specific retrieval models, and a test collection.

See below for a list of relevant publications, description of the task, background information, and challenges.

What are systematic reviews?

Systematic reviews are a type of literature review, synthesising all possible relevant information for highly focused research questions. In the medical domain, systematic reviews are the foundation of evidence based medicine and are of the highest quality evidence for this domain. Systematic reviews in the medical domain not only inform health care practitioners about what decisions to make about diagnosis and treatment, but have also been used to inform governmental policy making. The main type of systematic review seeks to systematically search and critically appraise and synthesise evidence from clinical studies (i.e., randomised controlled trials). There are, however, also a number of other types of systematic reviews, such rapid reviews (where time is a more important factor), scoping reviews (which synthesise a range of many broad areas of literature), or umbrella reviews (which could be though of as systematic reviews of systematic reviews).

Cost Factors

There are a number of considerations to make when conducting a systematic review. Most importantly are the time and cost factors involved. A systematic review has a number of steps which must be completed in a systematic nature. These steps are usually defined well in advance and strictly adhered to during the construction of the review. At a high level, these steps are as follows:

The main cost of a systematic review appears in step 4, where studies are screened and appraised to determine their inclusion or exclusion criteria for the following step. Often, a search strategy retrieves thousands, if not tens of thousands of results. The systematic nature of these reviews calls for inspecting each and every result retrieved. It is also common for this screening and appraisal to be performed in parallel with multiple reviewers to reduce bias (increasing the cost further). This screening process can often take months, and sometimes a year or more (depending on how many results are retrieved).

Reducing Cost Factors

There has been much research developing tools to assist researchers undertaking a systematic review to reduce the monetary and time costs of the review. Typically these tools help by assisting to prepare and maintain reviews, re-ranking results through active learning, automating evidence synthesis, among others. There has also been much research to develop methods for automatically prioritising the results in the screening and appraisal phase. Systematic review literature search is unlike typical web search (e.g., Google) as a Boolean retrieval model is used. Most research on ranking in the Information Retrieval domain has focused primarily on this “ad-hoc” task of ranking documents for a query similar to one that would be issued to a modern search engine. Ranking the results of a Boolean query cannot be performed with the same principals, and there has been many studies showing the ineffectiveness of Boolean queries vs. the types of queries used in modern search engines. The screening prioritisation for systematic review literature search therefore uses approaches such as active learning, rather than improving the retrieval model.

A Boolean query allows for the complete control over the search results. While it does not provide a mechanism for ranking, the trade-off in specifying exactly what should be retrieved through the use of set-based operators, term matching, and field restrictions allows for expert control. Furthermore, many search engines used for systematic review literature search (e.g., PubMed) also incorporate medical ontologies (i.e., MeSH), explicit stemming, and complex Boolean operators such as adjacency. These features of the types of Boolean queries used in this domain are also the reason the cost of a review are so high: the complexity involved in constructing a Boolean query to effectively satisfy the information need of the review is extremely high.

Improving Boolean Query Formulation

There are significant time and cost savings to be had by improving the effectiveness of Boolean queries. A more effective Boolean query retrieves less irrelevant studies while maintaining the number of relevant studies. Screening prioritisation only helps to bubble the most relevant studies to the top of the list; reviewers still must screen all studies systematically. A more effective query translates to less studies to screen overall. Even small decreases in numbers of irrelevant studies can significantly reduce cost and time factors of systematic review construction. Decreases in the time it takes to construct systematic reviews can lead to more accurate and up-to-date evidence based medicine; improving decisions by health care professionals.

Relevant Publications

2024

Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation
Xinyu Mao, Shengyao Zhuang, Bevan Koopman, Guido Zuccon
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval  ·  10 Jul 2024  ·  10.1145/3626772.3657921
Zero-Shot Generative Large Language Models for Systematic Review Screening Automation
Shuai Wang, Harrisen Scells, Shengyao Zhuang, Martin Potthast, Bevan Koopman, Guido Zuccon
Lecture Notes in Computer Science  ·  01 Jan 2024  ·  10.1007/978-3-031-56027-9_25

2023

Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Shuai Wang, Harrisen Scells, Bevan Koopman, Martin Potthast, Guido Zuccon
Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region  ·  26 Nov 2023  ·  10.1145/3624918.3625322
A Systematic Review of Cost, Effort, and Load Research in Information Search and Retrieval, 1972–2020
Molly McGregor, Leif Azzopardi, Martin Halvey
ACM Transactions on Information Systems  ·  18 Aug 2023  ·  10.1145/3583069
Outcome-based Evaluation of Systematic Review Automation
Wojciech Kusa, Guido Zuccon, Petr Knoth, Allan Hanbury
Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval  ·  09 Aug 2023  ·  10.1145/3578337.3605135
Smooth Operators for Effective Systematic Review Queries
Harrisen Scells, Ferdinand Schlatt, Martin Potthast
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval  ·  18 Jul 2023  ·  10.1145/3539618.3591768
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?
Shuai Wang, Harrisen Scells, Bevan Koopman, Guido Zuccon
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval  ·  18 Jul 2023  ·  10.1145/3539618.3591703
MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction
Shuai Wang, Hang Li, Guido Zuccon
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining  ·  27 Feb 2023  ·  10.1145/3539597.3573025

2022

Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search
Shuai Wang, Harrisen Scells, Bevan Koopman, Guido Zuccon
Proceedings of the 26th Australasian Document Computing Symposium  ·  15 Dec 2022  ·  10.1145/3572960.3572980
The Impact of Query Refinement on Systematic Review Literature Search
Harrisen Scells, Connor Forbes, Justin Clark, Bevan Koopman, Guido Zuccon
Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval  ·  23 Aug 2022  ·  10.1145/3539813.3545143
Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital
Han Chang Lim, Jodie A. Austin, Anton H. van der Vegt, Amir Kamel Rahimi, Oliver J. Canfell, …, Jason D. Pole, Michael A. Barras, Tobias Hodgson, Sally Shrapnel, Clair M. Sullivan
Applied Clinical Informatics  ·  01 Mar 2022  ·  10.1055/s-0042-1743243
Seed-Driven Document Ranking for Systematic Reviews: A Reproducibility Study
Shuai Wang, Harrisen Scells, Ahmed Mourad, Guido Zuccon
Advances in Information Retrieval  ·  01 Jan 2022  ·  https://doi.org/10.1007/978-3-030-99736-6_46

2021

MeSH Term Suggestion for Systematic Review Literature Search
Shuai Wang, Hang Li, Harrisen Scells, Daniel Locke, Guido Zuccon
Australasian Document Computing Symposium  ·  09 Dec 2021  ·  10.1145/3503516.3503530

2020

Systematic Review Automation Tools for End-to-End Query Formulation
Hang Li, Harrisen Scells, Guido Zuccon
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  ·  25 Jul 2020  ·  10.1145/3397271.3401402
Sampling Query Variations for Learning to Rank to Improve Automatic Boolean Query Generation in Systematic Reviews
Harrisen Scells, Guido Zuccon, Mohamed A. Sharaf, Bevan Koopman
Proceedings of The Web Conference 2020  ·  20 Apr 2020  ·  10.1145/3366423.3380075
Automatic Boolean Query Formulation for Systematic Review Literature Search
Harrisen Scells, Guido Zuccon, Bevan Koopman, Justin Clark
Proceedings of The Web Conference 2020  ·  20 Apr 2020  ·  10.1145/3366423.3380185
A Computational Approach for Objectively Derived Systematic Review Search Strategies
Harrisen Scells, Guido Zuccon, Bevan Koopman, Justin Clark
Lecture Notes in Computer Science  ·  01 Jan 2020  ·  10.1007/978-3-030-45439-5_26
You Can Teach an Old Dog New Tricks: Rank Fusion applied to Coordination Level Matching for Ranking in Systematic Reviews
Harrisen Scells, Guido Zuccon, Bevan Koopman
Lecture Notes in Computer Science  ·  01 Jan 2020  ·  10.1007/978-3-030-45439-5_27

2019

Automatic Boolean Query Refinement for Systematic Review Literature Search
Harrisen Scells, Guido Zuccon, Bevan Koopman
The World Wide Web Conference  ·  13 May 2019  ·  10.1145/3308558.3313544

2018

Improving Systematic Review Creation With Information Retrieval
Harrisen Scells
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval  ·  27 Jun 2018  ·  10.1145/3209978.3210226
Generating Better Queries for Systematic Reviews
Harrisen Scells, Guido Zuccon
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval  ·  27 Jun 2018  ·  10.1145/3209978.3210020
Query Variation Performance Prediction for Systematic Reviews
Harrisen Scells, Leif Azzopardi, Guido Zuccon, Bevan Koopman
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval  ·  27 Jun 2018  ·  10.1145/3209978.3210078

2017

Integrating the Framing of Clinical Questions via PICO into the Retrieval of Medical Literature for Systematic Reviews
Harrisen Scells, Guido Zuccon, Bevan Koopman, Anthony Deacon, Leif Azzopardi, Shlomo Geva
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management  ·  06 Nov 2017  ·  10.1145/3132847.3133080
A Test Collection for Evaluating Retrieval of Studies for Inclusion in Systematic Reviews
Harrisen Scells, Guido Zuccon, Bevan Koopman, Anthony Deacon, Leif Azzopardi, Shlomo Geva
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval  ·  07 Aug 2017  ·  10.1145/3077136.3080707
Reducing Workload of Systematic Review Searching and Screening Processes
Harrisen Scells
Electronic Workshops in Computing  ·  01 Jan 2017  ·  10.14236/ewic/fdia2017.2