asreview

To help you get started, we've collected the most common ways that asreview is being used within popular public projects.

msdslab / automated-systematic-review / tests / test_query.py View on Github

def test_query(query_strategy, n_features=50, n_sample=100,
               n_instances_list=[0, 1, 5, 50], n_train_idx=[0, 1, 5, 50]):
    classifier = get_model("rf")

    query_model = get_query_model(query_strategy)
    X = np.random.rand(n_sample, n_features)

    y = np.concatenate((np.zeros(n_sample//2), np.ones(n_sample//2)), axis=0)
    print(X.shape, y.shape)
    order = np.random.permutation(n_sample)
    print(order.shape)
    X = X[order]
    y = y[order]
    sources = query_strategy.split('_')

    classifier.fit(X, y)

    assert isinstance(query_model.param, dict)
    assert query_model.name == query_strategy

    for n_instances in n_instances_list:

View more ways to use asreview

ASReview: Active learning for Systematic Reviews

Systematically screening large amounts of textual data is time-consuming and often tiresome. The rapidly evolving field of Artificial Intelligence (AI) has allowed the development of AI-aided pipelines that assist in finding relevant texts for search tasks. A well-established approach to increasing efficiency is screening prioritization via Active Learning.

The Active learning for Systematic Reviews (ASReview) project, published in Nature Machine Intelligence implements different machine learning algorithms that interactively query the researcher. ASReview LAB is designed to accelerate the step of screening textual data with a minimum of records to be read by a human with no or very few false negatives. ASReview LAB will save time, increase the quality of output and strengthen the transparency of work when screening large amounts of textual data to retrieve relevant information. Active Learning will support decision-making in any discipline or industry.

ASReview software implements three different modes:

• Oracle Screen textual data in interaction with the active learning model. The reviewer is the 'oracle', making the labeling decisions.
• Exploration Explore or demonstrate ASReview LAB with a completely labeled dataset. This mode is suitable for teaching purposes.
• Simulation Evaluate the performance of active learning models on fully labeled data. Simulations can be run in ASReview LAB or via the command line interface with more advanced options.

Installation

The ASReview software requires Python 3.8 or later. Detailed step-by-step instructions to install Python and ASReview are available for Windows and macOS users.

pip install asreview

Upgrade ASReview with the following command:

pip install --upgrade asreview

To install ASReview LAB with Docker, see Install with Docker.

How it works

Getting started

Getting Started with ASReview LAB.

Citation

If you wish to cite the underlying methodology of the ASReview software, please use the following publication in Nature Machine Intelligence:

> van de Schoot, R., de Bruin, J., Schram, R. et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7

For citing the software, please refer to the specific release of the ASReview software on Zenodo https://doi.org/10.5281/zenodo.3345592. The menu on the right can be used to find the citation format of prevalence.

For more scientific publications on the ASReview software, go to asreview.ai/papers.

Contact

For an overview of the team working on ASReview, see ASReview Research Team. ASReview LAB is maintained by Jonathan de Bruin and Yongchao Terry Ma.

The best resources to find an answer to your question or ways to get in contact with the team are:

• Documentation - asreview.readthedocs.io
• Newsletter - asreview.ai/newsletter/subscribe
• Quick tour - ASReview LAB quick tour
• Issues or feature requests - ASReview issue tracker
• FAQ - ASReview Discussions
• Donation - asreview.ai/donate
• Contact - asreview@uu.nl

License

The ASReview software has an Apache 2.0 LICENSE. The ASReview team accepts no responsibility or liability for the use of the ASReview tool or any direct or indirect damages arising out of the application of the tool.