AutoGen Multi-Agent Preventative Healthcare Team

Overview

This is a multi-agent system built on top of AutoGen agents designed to automate and optimize preventative healthcare outreach. It uses multiple agents, Large Language Models (LLMs), and asynchronous programming to streamline the process of identifying patients who meet specific screening criteria and generating personalized outreach emails.

Credit: Though heavily modified, the original idea came from Mike Lynch’s Medium blog.

AMD Solution Blueprints are packaged as Helm charts for deployment on a Kubernetes cluster. For development or further exploration, the source code is public and available in the Solution Blueprints GitHub repository.

Architecture

The blueprint integrates a Streamlit web application with a multi-agent AutoGen workflow and an AIM LLM service. By default, the AIM deploys Llama 3.3 70B for agent conversations and outreach email generation. The application pod does not require a GPU.

Component	Role
Streamlit UI	Web interface for uploading patient data, running analysis, and downloading outreach emails
AutoGen agents	Multi-agent orchestration
AIM LLM	Inference for screening criteria, patient filtering, and email generation

The workflow proceeds in three stages:

1. Define screening criteria: After getting the general screening task from the user, the User Proxy Agent starts a conversation between the Epidemiologist Agent and the Doctor Critic Agent to define the criteria for patient outreach based on the target screening type. The resulting criteria include age range (e.g., 40–70), gender, and relevant medical history.

2. Select and identify patients based on the screening criteria: The Assistant Agent filters patient data from a CSV file based on the defined criteria, including age range, gender, and medical conditions. The patient data were synthetically generated. You can find the sample data under data/patients.csv.

3. Generate outreach emails: The program generates outreach emails for the filtered patients using LLMs and saves them as text files.

Key Features

• Multi-agent workflow

• Personalized outreach email generation using on-premises AIM LLMs

• Streamlit web UI for upload, analysis, and download of draft emails

• Flexible LLM configuration — deploy the bundled AIM or connect to an existing service

Getting Started

This is a quick start guide on how to deploy the blueprint. For advanced options, such as reusing an existing AIM, providing a Hugging Face token, or overriding storage classes, see Deploying Solution Blueprints with Helm or explore the advanced deployment guide.

This blueprint supports AMD Instinct (default) and AMD Radeon platforms. The section below covers the default Instinct deployment. For Radeon and other advanced options, see:

• Deploy on AMD Radeon

Prerequisites

System Requirements

The blueprint requires the following cluster resources by default:

Resource	Default Configuration
GPUs	1 (AIM LLM; application pod does not require a GPU)
CPUs	5 CPU cores
RAM	68 Gi

To deploy to the Kubernetes cluster, ensure the following prerequisites are met:

• kubectl: Installed and configured to communicate with the cluster

• Helm 3.17 or higher: Installed on your local machine

Deployment

Solution Blueprints are packaged as OCI-compliant Helm charts in the Docker Hub registry and can be deployed to a Kubernetes cluster with a single command. Define the name (deployment name) and namespace (Kubernetes namespace), then pipe the output of helm template to kubectl apply -f -:

name="my-deployment"
namespace="my-namespace"
helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-preventative-healthcare \
  | kubectl apply -f - -n $namespace

Note: You can create a namespace using kubectl create namespace $namespace.

To check the status of the deployment, run:

kubectl get pods -n $namespace

Wait until all pods report Running and Ready.

Connect to UI

To connect to the UI, port-forward to 8501. The UI will then be available at http://localhost:8501 in your browser.

kubectl port-forward services/aimsb-preventative-healthcare-${name} 8501:80 -n $namespace

Once connected, use the application as follows:

1. Upload a tabular dataset of patient records. A sample dataset is included by default.

2. Provide the context of the screening task, e.g., diabetes screening.

3. Run the analysis.

4. Click Generate Outreach Emails to create draft emails to patients (.txt files with email drafts) and download them.

Clean Up

When you are finished, remove the deployed resources:

helm template $name oci://registry-1.docker.io/amdenterpriseai/aimsb-preventative-healthcare \
  | kubectl delete -f - -n $namespace

Disclaimer

This tool is for research and educational use only. It is not intended for clinical diagnosis or treatment.

Third-Party Components

This Solution Blueprint utilizes multiple components. For third-party license information, refer to each component’s documentation. Key third-party components are listed below.

Component	License
AutoGen	MIT
Streamlit	Apache 2.0

Terms of Use

AMD Solution Blueprints are released under the MIT License, which governs the parts of the software and materials created by AMD. Third-party software and materials used within the Solution Blueprints are governed by their respective licenses.