AI/ML based Python App and a Dashboard for Medical Practitioners
What?
- Development of a deployable AI/ML app that will predict the probablility of being infected with a virus based on various parameters obtained from blood test !
- Importantly being able to explain AI based diagnosis with Explainable AI (XAI) technologies !
How?
By developing machine learning strategies in order to effectively build a machine learning model involving huge amount of medical data. The following key technologies were used to achieve the objectives:
- Streamlit for UI/Dashboard
- Azure / Heroku clouds for app deployment
- Python for the development of predictive maintenance app
- Shap XAI for AI explainability & to boost confidence and trust
- Regression algorithms for building medical AI models
- XGBoost / Scikit-Learn frameworks for experimenting/building machine learning models
- Azure PostgreSql & Blob Storage for maintaining medical data & AI artifacts
- Docker for application isolation towards deployment
- Gitlab CI/CD for continuous integration & continuous deployment
May I see it ?
The following video shows a minimal version of the AI based medical diagnosis dashboard. The application is deployed in Heroku, and can be accessed anonymously. As the application sleeps while it is not actively used, the first load often takes some time.