Our goal is to provide clinicians with a powerful decision making tool that enhances clinical understanding of patient-level data. The PredictMod platform utilizes machine learning tools and complex datasets based on electronic health records, gut microbiome, and -omics data to forecast patient outcomes, often in response to treatment for a particular condition. While our primary condition of interest is Prediabetes, the tool is designed to be used for a variety of conditions, interventions, and data types.
The PredictMod web app was designed using Figma and implemented using a JavaScript Vue/Vuetify/Vite framework. For more information on these, see the links below:
This repository can be cloned directly using the following command:
git clone https://github.com/GW-HIVE/PredictMod.git
Ensure you have docker running in your environment. Then, navigate to the PredictMod directory and use the following commands to: 1 - Install the necessary dependencies 2 - Transpile/Compile the frontend 3 - Create the dockerized frontend 4 - Create the dockerized backend 5 - Launch the service
$ cd vuetify_app
$ npm install
$ cd ../scripts
$ ./compile_frontend.sh
$ ./build_frontend.sh
$ docker network create predictmod
$ ./run_frontend.sh
$ ./run_backend.sh
Then open a browser and navigate to http://localhost:4244/ to begin interacting with the dockerized application.
Prepare your own virtually environment using the requirement.txt file then activate your environment. See virtualenvwrapper docs for a primer on creating virtual environments.
Navigate to the PredictMod directory and use the following commands to: 1 - Install the necessary dependencies 2 - Transpile/Compile the frontend 3 - Run the server locally
$ pwd
<path/to/repo/>/PredictMod
$ cd vuetify_app
$ npm install
$ cd ../scripts
$ ./compile_frontend.sh
$ cd ../django_server
$ python manage.py runserver
Then navigate to http://localhost:8000/ and to begin working with the interface.
"Hidden" Dependencies
The most current version of the sheetJS (CE) library (currently 0.20.0) should be installed manually, as so:
npm i --save https://cdn.sheetjs.com/xlsx-0.20.0/xlsx-0.20.0.tgz
See SheetJS documentation for additional details.
Navigate to the upload-api directory and execute the following command to start the server:
npm start
Navigate to the vue-upload directory and execute the following command to start the app:
npm run serve
Now a link should appear, which you can follow to see the client side of the app
NB: The application will not have an analytical backend supporting it if run through npm, so no Python analysis will take place in this mode.
Holds files that are currently not in use, but may be useful to reference at a later time.
Contains all resources related to running the Django webserver middleware.
Model-specific documentation as well as UI content.
Contains all resources required to run the Flask webserver and analysis backend.
Work related to PredictMod UI.
Documentation, scripts, BCOs, and datasets for all models.
Convenience scripts to compile/transpile the frontend and to launch the application.
Contains all resources required to create & run the frontend.
