Automatic Risk of Bias assessment
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README.md

RobotReviewer

Automatic extraction of data from clinical trial reports

A simple webserver written in Python which accepts a clinical trial (in plain text/JSON), and returns risk of bias judgements.

The current release has a DOI: DOI

Systematic review author?

This software is the web-service version, meaning it's aimed at people who make systematic review software.

For most systematic review authors, if you want to try out RobotReviewer, you'd probably be better using the demo version on our website, available here. If you like it, you could email the person who maintains your systematic review software a link to this site - they might be interested in adding it.

(Alternatively, individual authors who are adept at installing unix software from the terminal are free to install this version on their own machines by following the optional 'Web UI' instructions below).

Developers of systematic review software?

RobotReviewer is open source and free to use under the GPL licence, version 3.0 (see the LICENCE.txt file in this repository).

We offer RobotReviewer free of charge, but we'd be most grateful if you would cite us if you use it. We're academics, and thrive on links and citations! Getting RobotReviewer widely used and cited helps us obtain the funding to maintain the project and make RobotReviewer better.

It also makes your methods transparent to your readers, and not least we'd love to see where RobotReviewer is used! :)

We'd appreciate it you could:

  1. Display the text, 'Risk of Bias automation by RobotReviewer (how to cite)' on the same screen or webpage on which the RobotReviewer results (highlighted text or risk of bias judgements) are displayed.
  2. For web-based tools, the text 'how to cite' should link to our website http://vortext.systems/robotreviewer
  3. For desktop software, you should usually link to the same website. If this is not possible, you may alternately display the text and example citations from the 'How to cite RobotReviewer' section below.

You can cite RobotReviewer as:

Marshall IJ, Kuiper J, & Wallace BC. RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. Journal of the American Medical Informatics Association 2015. doi:10.1093/jamia/ocv044

A BibTeX entry for LaTeX users is

@article{RobotReviewer2015,
  title = {{RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials}},
  author = {Marshall, Iain J and Kuiper, Jo\"{e}l and Wallace, Byron C},
  doi = {10.1093/jamia/ocv044},
  url = {http://dx.doi.org/10.1093/jamia/ocv044},
  journal = {Journal of the American Medical Informatics Association},
  year = {2015}
  month = jun,
  pages = {ocv044}
}

Installation

  1. Ensure you have a working version of Python 2.7. We recommend using Python from the Anaconda Python distribution for a quicker and more reliable experience. However, if you have Python 2.7 already installed that will probably work fine too.

  2. Get a copy of the RobotReviewer repo, and go into that directory

    git clone https://github.com/ijmarshall/robotreviewer.git
    cd robotreviewer
  3. Install the PDF web viewer (optional --- this is not needed if you want to just use the REST API)

    git submodule update --init --recursive
  4. Install the Python libraries that RobotReviewer needs - do one of the following.

    a. If you are using Anaconda: conda install flask numpy scipy scikit-learn nltk

    b. For everyone else: pip install flask numpy scipy scikit-learn nltk

  5. Install the sentence processing data:

    python -m nltk.downloader punkt

Running

The following

python robot.py

will start a flask server running on http://localhost:5000. You can run the server in development mode by passing DEBUG=true python robot.py.

REST API

Send some JSON by POST to /annotate such as:

{"text": "Put the full text of a clinical trial extracted from the PDF in here"}

and it will return something like:

{"marginalia": [
   {"title":"Random sequence generation",
    "type":"Risk of Bias",
    "description":"**Overall risk of bias prediction**: low",
    "annotations":[
       {"content":"A central pharmacy randomly assigned study medication in a 1:1 ratio using a computer-generated randomization sequence with variable-sized blocks ranging from 2 to 8 stratified by study site.",
        "uuid":"6e97f8d0-2970-11e5-b5fe-0242ac110006"
       }, ...

Running as a Python module

We will add full python module functionality to a future relase. However, the current code can easily be called directly from existing python code as follows:

import biasrobot # from the robotreviewer root directory

bot = biasrobot.BiasRobot()
text = "Put the full text of a clinical trial in here..."
annotations = bot.annotate(text)

Where the BiasRobot.annotate() method returns a "marginalia" dict of the same structure as the JSON example above.

Help

Feel free to contact us on mail@ijmarshall.com with any questions.

References

  1. Marshall, I. J., Kuiper, J., & Wallace, B. C. (2015). RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. Journal of the American Medical Informatics Association. [doi]
  2. Marshall, I., Kuiper, J., & Wallace, B. (2015). Automating Risk of Bias Assessment for Clinical Trials. IEEE Journal of Biomedical and Health Informatics. [doi]
  3. Kuiper, J., Marshall, I. J., Wallace, B. C., & Swertz, M. A. (2014). Spá: A Web-Based Viewer for Text Mining in Evidence Based Medicine. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2014) (Vol. 8726, pp. 452–455). Springer Berlin Heidelberg. [doi]
  4. Marshall, I. J., Kuiper, J., & Wallace, B. C. (2014). Automating Risk of Bias Assessment for Clinical Trials. In Proceedings of the ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) (pp. 88–95). ACM. [doi]

Copyright (c) 2015 Iain Marshall, Joël Kuiper, and Byron Wallace