Sytora

Sytora is a multilingual symptom-disease classification app. Translation is managed through the UMLS coding standard. A multinomial Naive Bayes classifier is trained on a handpicked dataset, which is freely available under CC4.0.

To get started:

• Clone this repo
• Install requirements
• Run the scripts (see below) and npm dependencies
• Get a UMLS license to download UMLS lexica & generate DB (umls.sh)
• Run and check http://localhost:5001
• Done! 🎉

Check out sytora.com for a demo.

Motivation

Finding the right diagnosis cannot be achieved by extracting symptoms and running a classification algorithm. The hardest part is asking the right questions, focusing what is important in the situation, connecting other events, and much more. Despite all this, I have long been exited about writing a symptom-disease lookup system to quickly gather related symptoms to symptoms etc. Not everything the model outputs is nonsense. Actually it helps a lot to quickly get a list of diseases given to a set of symptoms.

Data

The data is formatted as CSV files. Example entry:

Disease,Symptom
C0162565,C0039239

Data sources:

• DiseaseSymptomKB.csv: extracted from Disease-Symptom Knowledge Database. This data solely belongs to the respective authors. The authors are not not affiliated with this project.
• disease-symptom.csv: Manually created by hand. Freely available under CC 4.0.

Install

Training models & generating files from data:

1. Run cui2vec-converter.py to convert to GloVe-format. You need to get the pretrained embeddings first, available here: https://figshare.com/s/00d69861786cd0156d81. Place them in the data folder.
2. Run generateLabels.py to create the option labels for the select fields. Languages are currently hardcoded as list and can be extended if needed.
3. Run train.py to train a MNB classifier (for the disease prediction). Other necessary files are generated, too.
4. Run relatedSymptoms.py to train the model for the autosuggestion feature. This uses cui2vec. Please note that the authors of cui2vec are not affiliated with this code.

React client: cd into flaskapp and npm install. For development npm run watch, for production npm run build.

Flask Service

A small flask app is avaiable to showcase the trained models. cd into the flaskapp folder and start the app

python app.py

Deployment

Make sure to export REACT_APP_ENDPOINT with the correct address (e.g. http://yoursite.com)

Get going in ~10 min:

sudo apt update
sudo apt install python3-pip python3-dev build-essential libssl-dev libffi-dev python3-setuptools
sudo apt install python-pip python-dev
sudo apt install nodejs npm
pip install flask pandas sklearn numpy
pip install Flask-Limiter flask-expects-json
pip install more-itertools requests configparser
sudo apt-get install nginx supervisor

git clone https://github.com/leanderme/sytora
cd sytora/flaskapp && npm i

vi /etc/supervisor/conf.d/sytora.conf
sudo supervisorctl reread
sudo service supervisor restart
sudo supervisorctl status

sudo vim /etc/nginx/conf.d/virtual.conf
sudo nginx -t
sudo service nginx restart

sytora.conf:

[program:sytora]
directory=/root/sytora/flaskapp
command=gunicorn app:app -b 0.0.0.0:5001
autostart=true
autorestart=true
stderr_logfile=/var/log/sytora/sytora.err.log
stdout_logfile=/var/log/sytora/sytora.out.log

virtual.conf

server {
    listen       80;
    server_name  site.com;

    location / {
        proxy_pass http://127.0.0.1:8000;
    }
}

don't forget to transfer the umls.db, e.g. scp ./umls.db root@address:/root/sytora/flaskapp/umls/database

Coding quality, security & stability

This project was written very quickly with no performance or stability features in mind; the code base suffered accordingly. Expect things to be cleaned up soon though.

Please note that I'm a machine learning hobbyist and a medical student. The code may not in accordance with common conventions.

Acknowledgements

This project is heavily inspired by:

• https://github.com/Aniruddha-Tapas/Predicting-Diseases-From-Symptoms
• https://github.com/sekharvth/symptom-disease