cyberflow

COVID-19 public dataset on GCP from cases in Italy

by the Italian Society of Medical and Interventional Radiology (ISMIR)

This repository contains all the code required to extract relevant information from pdf documents published by ISMIR and store raw data in a relational database and entities in a No-SQL database.

In particular, you will use Google Cloud Vision API and Translation API, before storing the information on BigQuery. Separately, you will also use specific NER models (from Scispacy) to extract (medical) domain specific entities and store them in a NoSQL db (namely Datastore) on Google Cloud Platform.

Looking for more context behind this dataset? Check out this article.

Google Cloud Architecture of the pipeline:

Quick sneak peak on the Entity dataset on Datastore:

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Installation

You can replicate this pipeline directly on your local machine or on the cloud shell on GCP.

Requirements:

• Clone this repo to your local machine using https://github.com/azizketari/covid19_ISMIR.git
• You need a Google Cloud project and IAM rights to create service accounts.
• Create and Download the json key associated with your Service Account. Useful link

• Modify the values to each variables in env_variables.sh file then run

  source env_variables.sh
  

• Set the project that you will be working on:

gcloud config set project $PROJECT_ID

• Enable APIs:

  gcloud services enable vision.googleapis.com
  gcloud services enable translate.googleapis.com
  gcloud services enable datastore.googleapis.com
  gcloud services enable bigquery.googleapis.com
  

• Install package requirements:

  cd ~/covid19_ISMIR
  pip3 install --user -r requirements.txt
  

Note:

You will also need to download a Named Entity Recognition model for the second part of this pipeline. See Scispacy full selection of available models here. If you follow this installation guide, the steps will automatically download a model for you and install it.

Extracting data

• Step 1: Download the required files to your bucket and load the required model in your local
  (this step will take ~10 min)

  Optional: If you have already downloaded the scispacy model, you should modify the file ./content/download_content.sh to not repeat that step

  source ./content/download_content.sh
  pip install -U ./scispacy_models/en_core_sci_lg-0.2.4.tar.gz
  

• Step 2: Start the extraction of text from the pdf documents

python3 ./scripts/extraction.py

Pre-processing data

Following the extraction of text, it's time to translate it from Italian to English and curate it.

python3 ./scripts/preprocessing.py

Storing data

Following the pre-processing, it's time to store the data in a more searchable format: a data warehouse - BigQuery - for the text, and a No-SQL database - Datastore - for the (UMLS) medical entities.

python3 ./scripts/storing.py

Test

Last but not least, you can query your databases using this script.

python3 ./scripts/retrieving.py

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Contributing

To get started...

Step 1

• Option 1

  • 🍴 Fork this repo!
    

• Option 2

  • 👯 Clone this repo to your local machine using https://github.com/azizketari/covid19_ISMIR.git
    

Step 2

• HACK AWAY! 🔨🔨🔨

Step 3

• 🔃 Create a new pull request

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Citing

• ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing by Mark Neumann and Daniel King and Iz Beltagy and Waleed Ammar (2019)
  

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License

• MIT License