Open Source AI Research Tool

For detailed documentation to install Gregory on your server and up to date information, visit https://gregory-ai.com/.

If you have any questions, please open an issue or discussion on GitHub

Installing Gregory AI

This software is open source and available on GitHub, with instructions to install it using docker images.

Hardware requirements

Gregory MS is running on a Digital Ocean virtual private server.

  • 2 vCPU
  • 4 GB Memory
  • 80 GB Disk
  • Ubuntu 20.04 (LTS) x64

Software requirements

Access the information

RSS

There are RSS a number of RSS feeds you can use to access the database in real time:

Latest Articles

Latest Trials

Machine Learning Prediction

API Endpoints

The API is served using Django Rest Framework and can be accessed at https://api.gregory-ms.com/.

Articles

List all articles

https://api.gregory-ms.com/articles/all?format=json

List article that matches the {ID} number.

https://api.gregory-ms.com/articles/id/{ID}

Example: https://api.gregory-ms.com/articles/19

List all relevant articles.

These are articles that we show on the home page because they appear to offer new courses of treatment.

https://api.gregory-ms.com/articles/relevant

Articles’ Sources

List all articles from specified {source}.

https://api.gregory-ms.com/articles/source/{source_id}/

List all available sources.

https://api.gregory-ms.com/sources/

Trials

List all trials.

https://api.gregory-ms.com/trials/all?format=json

Example: https://api.gregory-ms.com/trials/all

Trials’ Sources

List all trials from specified {source}.

https://api.gregory-ms.com/trials/source/{source_id}

Example: https://api.gregory-ms.com/trials/source/12/

Database Structure

Articles

The JSON response contains information on scientific articles retrieved from multiple academic sources.

Available fields can be found at https://api.gregory-ms.com/articles/ by clicking the options button.

Trials

Data available at https://api.gregory-ms.com/trials/ by clicking the options button.

Resources

GitHub repository

Mobility Report from Apple Watch Data

The Mobility report is meant to be used as a snapshot of the patient’s mobility and walking assymetry using Apple’s HealthKit data from the Apple Watch.

This is an independent project that runs on the good will of volunteers

You can help by spreading the word or by making a donation to pay for the server costs.