cov-bsv 0.0.1.1

An NLP pipeline for COVID-19 surveillance used in the Department of Veterans Affairs Biosurveillance.

VA_COVID-19_NLP

An NLP pipeline for COVID-19 surveillance used in the Department of Veterans Affairs Biosurveillance. This system is described in A Natural Language Processing System for National COVID-19 Surveillance in the US Department of Veterans Affairs by Chapman et al as part of the ACL COVID-19 Emergency Workshop (pending review as of 7/6/2020).

Our system was deployed as part of the VA response to the COVID-19 pandemic and has helped to identify a large number of COVID-19 cases in the national VA healthcare system.

Disclaimer

This repository its not meant to be a complete, finalized system for detection of COVID-19 in clinical text. It was developed in the US Department of Veterans Affairs for a specific operational goal. However, performance will vary based on your data and use case. This is also not the exact system described in the paper above - it has been modified and simplified to be generalizable rather than being extremely specific to VA data.

You will likely need to customize your pipeline based on your data and needs. But hopefully we've made that easy to do! The tutorials in ./notebooks show how each component works, how you can add new rules, and how to remove/add components to the pipeline.

Overview

This repository implements a version of the system described in A Natural Language Processing System for National COVID-19 Surveillance in the US Department of Veterans Affairs (add link to open review.)

The NLP system is designed as a spaCy processing pipeline. Most components are implemented as part of the medSpaCy package.

The pipeline consists of the following main pieces:

1. Custom components imported from medSpaCy
2. A document classifier component which assigns the attribute doc._.cov_classification indicating whether a document "POS", "UNK", or "NEG" for COVID-19
3. A knowledge base containing rules which are added to the components and control which entities are extracted and what attributes are assigned

Usage

A detailed tutorial and explanation is provided in ./notebooks.

Installation

You can install cov_bsv by cloning this repository and installing using setup.py:

$ python setup.py install

Or by installing with pip (not available as of 7/2):

$ pip install cov-bsv

cov_bsv requires Python>=3.6

Quick Start

Below is a simple example which takes a clinical text, loads a pipeline with the knowledge base, and processes the text. We can then inspect the document classification and linguistic patterns extracted from the text using visualization functions from cov_bsv and medspacy.

import cov_bsv

nlp = cov_bsv.load(model="default", load_rules=True)
text = """
Patient presents to rule out COVID-19. 
His wife recently tested positive for novel coronavirus.​

COVID-19 results pending.​
"""

doc = nlp(text)

cov_bsv.visualize_doc(doc)

from medspacy.visualization import visualize_dep
doc = nlp("His wife recently tested positive for novel coronavirus.​")
visualize_dep(doc)

Contact Information

If you have any questions or comments, or if you have applied NLP to help combat the COVID-19 pandemic, we'd love to hear from you! Feel free to reach out using the contact information below.

• Primary Developer: Alec Chapman
• Email Address: [e-mail]