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

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.

• 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:

  
  
  Note:
  
  You will also need to download a NER model for the second part of this pipeline. See Scispacy full selection of 
  available models [here](https://allenai.github.io/scispacy/). If you follow this installation guide, the steps 
  will automatically download a model for you and install it.
  
  
  ## Extracting data
  - **Step 0:** Navigate to the cloned repo on your local machine
   
  `cd ~/covid19_ISMIR`
  
  - **Step 1:** Modify the values to each variables in env_variables.sh file then run
  
  

./env_variables.sh

- **Step 2:** Download the required files to your bucket and load the required model in your local  
(this step will take ~10 min)

sh ~/data/download_content.sh pip install -U ./scispacy_models/en_core_sci_lg-0.2.4.tar.gz ```

• Step 3: Start the extraction of text from the pdf documents
  

python3 extraction.py

Pre-processing data

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

python3 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 storing.py

Test

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

python3 retrieving.py

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