Active learning for Systematic Reviews¶
The rapidly evolving field of artificial intelligence (AI) has allowed the development of AI-aided pipelines that assist in finding relevant texts for such search tasks. A well-established approach to increase the efficiency of title and abstract screening is determining prioritization[2, 3] with active learning, which is very effective for systematic reviewing[5-15]. It works as follows: Just like with a classical pipeline, you start with the set of all unlabeled records (e.g., meta-data containing titles and abstracts of scientific papers) retrieved from a search (pool). This is followed by constructing a training set, which, in the example of systematic reviewing, consists of labeled data provided by the annotator (researcher) (e.g., some relevant and irrelevant abstracts). Then, the active learning cycle starts. Active learning denotes the scenario in which the reviewer is labeling references that are presented by a machine learning model. The machine learning model learns from the reviewers’ decision and uses this knowledge in selecting the next reference that will be presented to the reviewer. The annotated dataset starts out small and iteratively grows in size:
1. The first step is feature extraction. That is, an algorithm cannot make predictions from the records as they are; their textual content needs to be represented more abstractly. The algorithm can then determine the important features that are necessary to classify a record, thereby drastically decreasing the search space;
2. A specific classification algorithm (i.e, machine learning model) is then chosen;
3. The chosen classifier is trained on the labeled records and estimates relevance scores for all unlabeled records. The model chooses a record to show to the user for the next iteration of the cycle;
4. The annotator screens this record and provides a label, relevant or irrelevant. The newly labeled record is moved to the training data set and it’s back to the previous step.
The interaction with the human can be used to train a model with a minimum number of labeling tasks and the trained model is the output which is then used to classify new data, also called Human-in-the-Loop machine learning. In the general sense the key idea behind active learning is that, if you allow the model to decide for itself which data it wants to learn from, its performance and accuracy may improve and it requires fewer training instances to do so. Moreover, the dataset’s informativeness is increased by having the reviewer annotate those references that are more informative to the model (uncertainty-based sampling).
The application of active learning to systematic reviewing is called Researcher-In-The-Loop (RITL) with three unique components:
(I) The primary output of the process is a selection of the pool with only relevant papers;
(II) All data points in the relevant selection should have been seen by a human at the end of the process (certainty-based sampling);
(III) The process requires a strong, systematic way of working. As such, it entails several explicit and reproducible steps, as outlined in the PRISMA guidelines for systematic reviewing. This procedure ensures (more or less) that all likely relevant publications are found in a standardized way based on pre-defined eligibility criteria, extracting data from eligible studies, and synthesizing the results.
In the active learning cycle, the model incrementally improves its predictions on the remaining unlabeled records, but hopefully all relevant records are identified as early in the process as possible. The reviewer decides to stop at some point during the process to conserve resources or when all records have been labeled. In the latter case, no time was saved and therefore the main question is to decide when to stop: i.e. to determine the point at which the cost of labeling more papers by the reviewer is greater than the cost of the errors made by the current model. Finding 100% of the relevant papers appears to be almost impossible, even for human annotators. Therefore, we typically aim to find 95% of the inclusions. However, in the situation of an unlabeled dataset, you don’t know how many relevant papers there are left to be found. So researchers might either stop too early and potentially miss many relevant papers, or stop too late, causing unnecessary further reading. That is, one can decide to stop reviewing after a certain amount of non-relevant papers have been found in succession, but this is up to the user to decide.
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