The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses.

Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.

Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records.

BACKGROUND: Conducting a systematic review demands a significant amount of effort in screening titles and abstracts. To accelerate this process, various tools that utilize active learning have been proposed. These tools allow the reviewer to interact with machine learning software to identify relevant publications as early as possible. The goal of this study is to gain a comprehensive understanding of active learning models for reducing the workload in systematic reviews through a simulation study. METHODS: The simulation study mimics the process of a human reviewer screening records while interacting with an active learning model. Different active learning models were compared based on four classification techniques (naive Bayes, logistic regression, support vector machines, and random forest) and two feature extraction strategies (TF-IDF and doc2vec). The performance of the models was compared for six systematic review datasets from different research areas. The evaluation of the models was based on the Work Saved over Sampling (WSS) and recall. Additionally, this study introduces two new statistics, Time to Discovery (TD) and Average Time to Discovery (ATD). RESULTS: The models reduce the number of publications needed to screen by 91.7 to 63.9% while still finding 95% of all relevant records (WSS@95). Recall of the models was defined as the proportion of relevant records found after screening 10% of of all records and ranges from 53.6 to 99.8%. The ATD values range from 1.4% till 11.7%, which indicate the average proportion of labeling decisions the researcher needs to make to detect a relevant record. The ATD values display a similar ranking across the simulations as the recall and WSS values. CONCLUSIONS: Active learning models for screening prioritization demonstrate significant potential for reducing the workload in systematic reviews. The Naive Bayes + TF-IDF model yielded the best results overall. The Average Time to Discovery (ATD) measures performance of active learning models throughout the entire screening process without the need for an arbitrary cut-off point. This makes the ATD a promising metric for comparing the performance of different models across different datasets.

Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer.

BACKGROUND: Improving the speed of systematic review (SR) development is key to supporting evidence-based medicine. Machine learning tools which semi-automate citation screening might improve efficiency. Few studies have assessed use of screening prioritization functionality or compared two tools head to head. In this project, we compared performance of two machine-learning tools for potential use in citation screening. METHODS: Using 9 evidence reports previously completed by the ECRI Institute Evidence-based Practice Center team, we compared performance of Abstrackr and EPPI-Reviewer, two off-the-shelf citations screening tools, for identifying relevant citations. Screening prioritization functionality was tested for 3 large reports and 6 small reports on a range of clinical topics. Large report topics were imaging for pancreatic cancer, indoor allergen reduction, and inguinal hernia repair. We trained Abstrackr and EPPI-Reviewer and screened all citations in 10% increments. In Task 1, we inputted whether an abstract was ordered for full-text screening; in Task 2, we inputted whether an abstract was included in the final report. For both tasks, screening continued until all studies ordered and included for the actual reports were identified. We assessed potential reductions in hypothetical screening burden (proportion of citations screened to identify all included studies) offered by each tool for all 9 reports. RESULTS: For the 3 large reports, both EPPI-Reviewer and Abstrackr performed well with potential reductions in screening burden of 4 to 49% (Abstrackr) and 9 to 60% (EPPI-Reviewer). Both tools had markedly poorer performance for 1 large report (inguinal hernia), possibly due to its heterogeneous key questions. Based on McNemar's test for paired proportions in the 3 large reports, EPPI-Reviewer outperformed Abstrackr for identifying articles ordered for full-text review, but Abstrackr performed better in 2 of 3 reports for identifying articles included in the final report. For small reports, both tools provided benefits but EPPI-Reviewer generally outperformed Abstrackr in both tasks, although these results were often not statistically significant. CONCLUSIONS: Abstrackr and EPPI-Reviewer performed well, but prioritization accuracy varied greatly across reports. Our work suggests screening prioritization functionality is a promising modality offering efficiency gains without giving up human involvement in the screening process.

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization.

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.

Effective study selection using text mining or a single-screening approach: a study protocol.

BACKGROUND: Systematic information retrieval generally requires a two-step selection process for studies, which is conducted by two persons independently of one another (double-screening approach). To increase efficiency, two methods seem promising, which will be tested in the planned study: the use of text mining to prioritize search results as well as the involvement of only one person in the study selection process (single-screening approach). The aim of the present study is to examine the following questions related to the process of study selection: Can the use of the Rayyan or EPPI Reviewer tools to prioritize the results of study selection increase efficiency? How accurately does a single-screening approach identify relevant studies? Which advantages or disadvantages (e.g., shortened screening time or increase in the number of full texts ordered) does a single-screening versus a double-screening approach have? METHODS: Our study is a prospective analysis of study selection processes based on benefit assessments of drug and non-drug interventions. It consists of two parts: firstly, the evaluation of a single-screening approach based on a sample size calculation (11 study selection processes, including 33 single screenings) and involving different screening tools and, secondly, the evaluation of the conventional double-screening approach based on five conventional study selection processes. In addition, the advantages and disadvantages of the single-screening versus the double-screening approach with regard to the outcomes "number of full texts ordered" and "time required for study selection" are analyzed. The previous work experience of the screeners is considered as a potential effect modifier. DISCUSSION: No study comparing the features of prioritization tools is currently available. Our study can thus contribute to filling this evidence gap. This study is also the first to investigate a range of questions surrounding the screening process and to include an a priori sample size calculation, thus enabling statistical conclusions. In addition, the impact of missing studies on the conclusion of a benefit assessment is calculated. SYSTEMATIC REVIEW REGISTRATION: Not applicable.