To run this code, please upload it to Google Colab. Also, make sure you download the 100-dimensional GloVe embeddings trained on 6B words from https://nlp.stanford.edu/projects/glove/

The SWIFT review datasets can be retrieved from Howard, B.E., Phillips, J., Miller, K. et al. SWIFT-Review: a text-mining workbench for systematic review. Syst Rev 5, 87 (2016). https://doi.org/10.1186/s13643-016-0263-z The drug review datasets can be retrieved from Cohen AM, Hersh WR, Peterson K, Yen PY. Reducing Workload in Systematic Review Preparation Using Automated Citation Classification. JAMIA 2006: https://dmice.ohsu.edu/cohenaa/systematic-drug-class-review-data.html

Please download the datasets and develop your own script using Entrez (https://biopython.org/docs/1.75/api/Bio.Entrez.html) to obtain title and abstracts. Some articles do not contain an abstract, as they are interviews or book chapters.

For citing this code, please refer to van Dinter, R., Catal, C., & Tekinerdogan, B. (2021). A Multi-Channel Convolutional Neural Network approach to automate the citation screening process. Applied Soft Computing, 112, 107765.

This code was tested with Python 3.7.11 on Ubuntu 18.04.

conda create -n cnn python=3.7.11

If you plan to run the code on a GPU install packages using conda:

% conda install -c anaconda tensorflow-gpu==2.4.1 scikit-learn pandas nltk 
% pip install keras==2.4.3 openpyxl seaborn gensim==3.8.3

You need to also download data for nltk:

import nltk

nltk.download(['stopwords'])