The Drug Therapy Risk Prediction Accelerator was created for COVID-19 drug therapy therefore you will see COVID-19 mentioned in this repository. This accelerator can be used for other drugs. It is an end-to-end analytics platform that predicts the likelihood of a patient to experience a fatal adverse event (death) after receiving drug therapy. The accelerator incorporates real-world data from the State Drug Utilization Database (SDUD) and the FDA Adverse Events Database (FAERS).

The accelerator uses the SDUD data to first build a retrospective analysis to identify drug prescription trends between the years 2018-2021, particularly during the onset of COVID-19 in 2019. Next, a prospective analysis looks at FDA adverse events associated with these drugs between 2019-2021. A machine learning (ML) model is built to predict the likelihood of a fatal adverse event, and identify the patient characteristics associated with this outcome.

The accelerator can be used in a provider organization covering multiple personas, ranging from backend engineers to frontend clinical users.

For technical users, a Data Engineer can quickly ingest SDUD and FAERS data through a low-code, no-code experience. A Data Architect can use a flexible, scalable analytics platform to query a data lake, anonymize sensitive patient information, and generate operational reports using familiar SQL language. The Data Analyst uses a visualization platform to plot trends observed in the data. Professional Data Scientists and Citizen Data Scientists have the ability to build and train machine learning models using traditional and/or automated ML methods, and register and deploy the model for real-time inferencing.

For clinical users, an informatics team at a hospital may be interested in building a customized clinical surveillance dashboard to observe nationwide trends for clinical adverse events. A patient may be interested in understanding their own risk for taking off-label drugs for symptomatic disease. A physician can input patient characteristics into the model to assess drug risk, with the goal of providing the right therapy to the right patient at the right time.

The architecture diagram below details what will be built with this accelerator.

To use this accelerator, you will need to have access to and/or provision the following applications:

1. Access to an Azure Subscription
2. Power Apps License (or free trial)
3. PowerShell 7.1
4. Azure CLI
5. Databricks CLI

A working knowledge of Azure, Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Machine Learning, Azure Container Services, and PowerBI will also be helpful. To learn more about these services, please visit:

1. Azure Data Factory
2. Azure Databricks
3. Azure Synapse Analytics
4. Azure Machine Learning
5. Azure Container Instances
6. Power BI

1. An ARM template deploys and configures all the resources required to run the accelerator. Click the Deploy to Azure button below:

   

   The only 2 required parameters during the set-up are:

   • Admin Password for the Synapse Dedicated SQL Pool being deployed.
   • Prefix ID to preface the resources with a unique name you can later reference.

2. Clone or download this repository and navigate to the project's root directory.

   Option 1: In Windows Terminal, clone the GitHub repo:

   git clone https://github.com/slsu0424/COVID19-Drug-Therapies.git

   Option 2: Navigate towards the top right of this screen, and expand the Code button menu:

   

3. Go to the Deployment Guide under ./02-Deployment to complete the solution setup.

NOTE: Be sure to manually pause the SQL pool when you are not using it from the Synapse Workspace to reduce cost. Follow the steps below to pause the SQL pool:

• Navigate to the Synapse workspace
• Go to Manage > SQL pools > Pause the SQL pool

This folder contains an Executive Overview and Technical Design for the accelerator. In addition, a video provides a detailed walkthrough of the accelerator setup.

This folder contains the scripts used to deploy the required resources into your Azure Subscription. This can be done using the scripts or Azure Portal. The Deployment Guide is provided to help complete the rest of the accelerator setup.

This folder contains the JSON templates needed to complete the data engineering tasks outlined in the Deployment Guide. You will use the content in this folder following the instructions in the Deployment Guide.

This folder contains the Notebooks and SQL Scripts needed to complete the tasks to prepare the data for operational reporting and reports. You will use the content in this folder following the instructions in the Deployment Guide.

This folder contains the Notebooks needed to complete the tasks for building a machine learning model. You will use the content in this folder following the instructions in the Deployment Guide.

This folder contains the Web-based User Interface to be used by a end user.

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This project is developed as part of the Microsoft Healthcare & Life Sciences Customer Success Unit Data Science & AI Transformational Projects Initiative.

Author: Sandy Su