MedspaCy Pneumonia NLP

Flexible medspaCy NLP pipeline for detecting assertions of pneumonia in different clinical notes.

Overview

This package implements a medspaCy pipeline for classifying pneumonia diagnoses in 3 types of clinical notes:

• Emergency note
• Radiology report
• Discharge summary

The system was primarily designed in Veterans Affairs and was then validated in University of Utah. Rules designed for University of Utah's Epic EHR are provided and can be added to the pipeline. Custom rules can be designed and added in a similar manner. See notebooks/ for detailed documentation and examples.

Quickstart

Installation

pip install medspacy_pna

Or:

python setup.py install

You will also need to install spaCy's en_core_web_sm model (or specify a different model when loading the NLP)

python -m spacy download en_core_web_sm

Usage

from medspacy_pna import build_nlp
from medspacy_pna.display import create_html
from IPython.display import HTML

# Load a model specific to either "emergency", "radiology", or "discharge"
nlp_emergency = build_nlp(domain="emergency")
note_emergency = """
History of Present Illness: Patient presents to ED with cough and fever.

Medical decision making: Differential diagnoses include pna and CHF. 
Will order a chest x-ray to r/o pneumonia.

Assessment/Plan:
Admit for suspected community-acquired pneumonia.
"""
doc_emergency = nlp_emergency(note_emergency)
HTML(create_html(doc_emergency, "emergency", document_classification=True))