ExaCT: automatic extraction of clinical trial characteristics from journal publications

<p>Abstract</p> <p>Background</p> <p>Clinical trials are one of the most important sources of evidence for guiding evidence-based practice and the design of new trials. However, most of this information is available only in free text - e.g., in journal publications - which is labour intensive to process for systematic reviews, meta-analyses, and other evidence synthesis studies. This paper presents an automatic information extraction system, called ExaCT, that assists users with locating and extracting key trial characteristics (e.g., eligibility criteria, sample size, drug dosage, primary outcomes) from full-text journal articles reporting on randomized controlled trials (RCTs).</p> <p>Methods</p> <p>ExaCT consists of two parts: an information extraction (IE) engine that searches the article for text fragments that best describe the trial characteristics, and a web browser-based user interface that allows human reviewers to assess and modify the suggested selections. The IE engine uses a statistical text classifier to locate those sentences that have the highest probability of describing a trial characteristic. Then, the IE engine's second stage applies simple rules to these sentences to extract text fragments containing the target answer. The same approach is used for all 21 trial characteristics selected for this study.</p> <p>Results</p> <p>We evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (<it>first stage</it>) was able to recover 88% of relevant sentences among its top five candidates (top5 recall) with the topmost candidate being relevant in 80% of cases (top1 precision). Precision and recall of the extraction rules (<it>second stage</it>) were 93% and 91%, respectively. Together, the two stages of the extraction engine were able to provide (partially) correct solutions in 992 out of 1050 test tasks (94%), with a majority of these (696) representing fully correct and complete answers.</p> <p>Conclusions</p> <p>Our experiments confirmed the applicability and efficacy of ExaCT. Furthermore, they demonstrated that combining a statistical method with 'weak' extraction rules can identify a variety of study characteristics. The system is flexible and can be extended to handle other characteristics and document types (e.g., study protocols).</p

The Adoption and Effectiveness of Automation in Health Evidence Synthesis

Background: Health systems worldwide are often informed by evidence-based guidelines which in turn rely heavily on systematic reviews. Systematic reviews are currently hindered by the increasing volume of new research and by its variable quality. Automation has potential to alleviate this problem but is not widely used in health evidence synthesis. This thesis sought to address the following: why is automation adopted (or not), and what effects does it have when it is put into use? / Methods: Roger’s Diffusion of Innovations theory, as a well-established and widely used framework, informed the study design and analysis. Adoption barriers and facilitators were explored through a thematic analysis of guideline developers’ opinions towards automation, and by mapping the adoption journey of a machine learning (ML) tool among Cochrane Information Specialists (CISs). A randomised trial of ML assistance in Risk of Bias (RoB) assessments and a cost-effectiveness analysis of a semi-automated workflow in the maintenance of a living evidence map each evaluated the effects of automation in practice. / Results: Adoption decisions are most strongly informed by the professional cultural expectations of health evidence synthesis. The stringent expectations of systematic reviewers and their users must be met before any other characteristic of an automation technology is considered by potential adopters. Ease-of-use increases in importance as a tool becomes more diffused across a population. Results of the randomised trial showed that ML-assisted RoB assessments were non-inferior to assessments completed entirely by human researcher effort. The cost-effectiveness analysis showed that a semi-automated workflow identified more relevant studies than the manual workflow and was less costly. / Conclusions: Automation can have substantial benefits when integrated into health evidence workflows. Wider adoption of automation tools will be facilitated by ensuring they are aligned with professional values of the field and limited in technical complexity