a shared space for humans and machines to work together to update systematic reviews.
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README.md

trial2rev

Synopsis

A shared space for humans and machines to capture links between systematic reviews and clinical trials

Getting Started

(Please note that there are a number of steps involved in getting the system up and running, and this guide is fairly minimal, assumes familiarity with the trial2rev website and experience working with the tools listed below, and is not intended for use in production. We may consider Dockerizing the system or providing a more comprehensive step-by-step guide in future if there is demand for this)

At the bare minimum you'll need the following for your development environment:

  1. Python
  2. PostgreSQL
  3. virtualenv
  4. RabbitMQ
  5. Git

Initialisation

The following assumes you have all of the recommended tools listed above installed. Note that this has only been tested on Centos 7 and Amazon Linux 2.

1. Clone the project:

$ git clone username@github.com:evidence-surveillance/trial2rev.git trial2rev

2. Create and initialize virtualenv for the project:

$ virtualenv trial2rev
$ cd trial2rev
$ source bin/activate
$ (trial2rev) pip install -r requirements.txt

3. Create config.py file for your local setup based on config.py.example:

Being sure to include all of the config values listed, including your PostgreSQL username & password, the name of the database you'll be creating (trial2rev in this example), flask secret key, EUtils API key, mail server details, etc.

4. Connect to PostgreSQL and create the database:

$ (trial2rev) psql postgres
$ postgres=> CREATE DATABASE trial2rev;
$ postgres=> \q

5. Create tables for the database:

$  psql -f test/data/srss.sql trial2rev

6. Populate the database:

You can populate the database with systematic reviews, trial registry entries, and trial publications by executing the relevant methods in remote_tasks.py. It is recommended that you first pull trial registry entries from ClinicalTrials.gov using

$ (trial2rev) python
$ >>> import remote_tasks as rt
$ >>> rt.update_tregistry_entries(5000)

which will pull all registered trials added or updated in the last 5000 days. Then, you may pull PubMed articles with automatic links to ClinicalTrials.gov:

$ >>> rt.update_trial_publications(5000)

and finally, pull systematic reviews that reference trial publications with links, or ClinicalTrials.gov directly:

$ >>> rt.populate_reviews(5000)

this will also automatically retrieve linked trials for each review found via CrossRef or CDSR

7. Make predictions:

First, you'll need to generate the tfidf matrix of trial registry text

$ >>> rt.update_tfidf()

then, you can trigger basicbot1 and basicbot2 which predict relevant trials based on document similarity

$ >>> rt.update_basicbot()
$ >>> rt.update_basicbot2()

once you have a number of reviews with a few included trials each, you can try running matrix factorization to predict addidional relevant trials for these reviews. Credit to @dsurian for the code, repo can be found here.

$ >>> import matfac_impl as mi
$ >>> mi.update_results()

8. Generate TSNE:

You'll recieve errors if you attempt to access the website without generating a TSNE plot of the trial registry tfidf scores. You can do this by calling the method:

$ >>> rt.regenerate_tsne()

Web server

Now, everything should be ready for you to start the webserver and browse your local version of trial2rev!

1. Run the app on your local machine:

$ (trial2rev) python run.py

2. Start the celery worker:

$ (trial2rev) celery -A app.celery_inst worker --loglevel=info

3. Log in:

The site should now be visible on http://127.0.0.1:5000

You can log in using the default user:

Once logged in, you can create new users here: http://127.0.0.1:5000/admin

Acknowledgements

This project is supported by supported by the AWS Cloud Credits for Research program.
I have incorporated modified and original code from the metapub library into ecitmatch_tools.py, which is licensed under the Apache 2.0 license. I have also used my own fork of the eutils library, to which I have added functionality to extract additional metadata from PubMed articles. I cannot be grateful enough to all contributors of both of these libraries, which have been absolutely instrumental in the development of this project.