We are proud to be the
November 30, 2024
Imperial College London, White City Campus
09:00 GMT
Registration
White City Campus
10:00 GMT
Opening speech by Professor Eric Yeatman, Chair of I-X
White City Campus
10:15 GMT
Opening speech by Sana Wajid, CDO of Fetch.ai
White City Campus
10:30 GMT
Opening speech by Satwik Kar, National Medical Science Liaison at Moderna
White City Campus
10:45 GMT
Speed network and Team Formation
White City Campus
11:15 GMT
Hacking Begins
White City Campus
13:00 GMT
Lunch
White City Campus
14:00 GMT
Interactive workshop
White City Campus
16:30 GMT
Coffee Break
White City Campus
17:00 GMT
Hacking Continues
White City Campus
12:00 GMT
Project Submissions and Lunch
White City Campus
13:00 GMT
Project Presentations
White City Campus
14:30 GMT
Judging
White City Campus
15:00 GMT
Closing Ceremony
White City Campus
15:30 GMT
Networking
White City Campus
Fetch.ai’s vision is to create a marketplace of dynamic applications. We are empowering developers to build on our platform that can connect services and APIs without any domain knowledge.
Our infrastructure enables ‘search and discovery’ and ‘dynamic connectivity’. It offers an open, modular, UI agnostic, self-assembling of services.
Our technology is built on four key components:
Agents - AI Agents are independent decision-makers that connect to the network and other agents. These agents can represent data, APIs, services, ML models and people.
Agentverse - serves as a development and hosting platform for these agents.
Fetchai sdk – seamlessly integrate your AI Agent into agentverse and empower dynamic connectivity with the Fetch.ai SDK
Fetch Network - underpins the entire system, ensuring smooth operation and integration.
Challenge statement
Explore the potential of intelligent, multi-agent systems with Fetch.ai, which provides a simple framework for creating your own AI Agents that can communicate and collaborate with one another. By leveraging Fetch.ai’s uAgent library and Agentverse platform, you’ll have the tools to build AI Agents capable of breaking through the limitations of siloed systems.
The Patient Journey Mapping Tool for Vaccine Hesitancy challenge would focus on building a solution that helps Medical Affairs understand and address the specific factors influencing vaccine hesitancy throughout a patient’s decision-making process.
Challenge: Mapping the Patient Journey to Overcome Vaccine Hesitancy
Background: Vaccine hesitancy can be influenced by a variety of factors, including personal beliefs, cultural background, previous health experiences, and exposure to misinformation. Understanding where patients are in their journey—from initial hesitancy to acceptance—can help Medical Affairs create targeted interventions and deliver messages that resonate at each stage.
Project Objective: Develop an AI-powered tool that
Maps patient journeys by analyzing diverse data sources like social media, patient forums, and survey data, identifying common milestones in the journey from hesitancy to vaccine acceptance.
Identifies key touchpoints where Medical Affairs can intervene effectively to provide supportive information, address misconceptions, or respond to frequently asked questions.
Personalizes engagement strategies based on where individuals or communities are in their journey.
Approach and Features:
Data Analysis and Segmentation: Gather and analyze data on public sentiment regarding vaccines, leveraging NLP to identify common concerns and misbeliefs. Segment patients by demographics, health conditions, and psychographic factors (e.g., health beliefs, sources of information) to tailor interventions.
Journey Mapping and Touchpoint Identification: Use AI to map common stages in the journey, from initial hesitation to active research, consultation with healthcare providers, and eventual acceptance or refusal. Identify touchpoints such as moments of heightened concern (e.g., news of side effects) where Medical Affairs could deliver impactful messages.
Dynamic Engagement Strategies: The tool could provide recommendations for Medical Affairs on the best timing, format, and messaging tone for each stage in the journey, ensuring support is offered at critical decision points. Customize strategies for specific subgroups, like age, risk level, or geographic location, based on their unique vaccine hesitancy drivers.
Feedback and Continuous Learning: Measure the effectiveness of various interventions through real-time feedback on engagement metrics, helping refine future approaches and identifying what resonates with hesitant groups. The tool could adjust its recommendations over time by learning from new patient data and insights, staying responsive to evolving attitudes.
Deliverables:
-Patient Journey Dashboard: A visualization tool displaying segmented patient journeys, highlighting critical decision points and suggested interventions.
-Engagement Strategy Module: An AI-driven assistant that generates tailored messaging and outreach suggestions for Medical Affairs based on patient sentiment trends and journey stages.
Benefits: The tool would empower Medical Affairs to:
Bring Your Agents to Agentverse with the Fetch.ai SDK :
Already have your own AI capable of solving tasks but don’t want to use the uAgents package? No problem! The Fetch.ai SDK provides a seamless solution to onboard your agents onto Agentverse. With the Fetch.ai SDK, you can deploy your AI agents to Agentverse and query them anytime. It also helps you find the right agents from Agentverse to tackle your specific tasks. For more details, visit the Fetchai SDK Github.
Additional Information : You can post all your queries in the #innovation-labs channel on Discord.
You have to submit your projects on Devpost
Creating a Multi-Agent System using Pre-built Fetch.ai AI Agents: Myth Buster Application
Build your application using the fetch.ai SDK: Weather Forecast Application
If you want to learn more about uAgents through articles, please Click Here.
You can find more interesting integrations and example on our gitHub Repo.
Fetch.ai tech stack
Product Overview
This flowchart can get you to where you want to be:
Quick start example
This file can be run on any platform supporting Python, with the necessary install permissions. This example shows two agents communicating with each other using the uAgent python library.
Read the guide for this code here ↗
from uagents import Agent, Bureau, Context, Model
class Message(Model):
message: str
sigmar = Agent(name="sigmar", seed="sigmar recovery phrase")
slaanesh = Agent(name="slaanesh", seed="slaanesh recovery phrase")
@sigmar.on_interval(period=3.0)
async def send_message(ctx: Context):
await ctx.send(slaanesh.address, Message(message="hello there slaanesh"))
@sigmar.on_message(model=Message)
async def sigmar_message_handler(ctx: Context, sender: str, msg: Message):
ctx.logger.info(f"Received message from {sender}: {msg.message}")
@slaanesh.on_message(model=Message)
async def slaanesh_message_handler(ctx: Context, sender: str, msg: Message):
ctx.logger.info(f"Received message from {sender}: {msg.message}")
await ctx.send(sigmar.address, Message(message="hello there sigmar"))
bureau = Bureau()
bureau.add(sigmar)
bureau.add(slaanesh)
if __name__ == "__main__":
bureau.run()Important links
Examples to get you started:
Judging Criteria
Winner Team Fetch.Ai Beyond Boundaries AI Agent Prize
500 GBP plus
Internship Interview
Second Prize
Smartest AI Agent Prize
250 GBP
Third Prize
Best use of Fetch.Ai Tech
250 GBP
Support
Support will be available at the hackathon, and you can also reach out to the core dev team who will be able to support you via Discord ↗
Judges
Sana Wajid
Chief Development Officer
Satwik Kar
National Medical Science Liaison
Moderna
Dr. Anna Radomska
Head of Operations
Imperial College London
Prof Alessandra Russo
Head of Department of Computing
Imperial College London
Redvers Lee
Senior Client Growth Manager
Imperial College London
Mentors
Zoltan Mezei
AI Engineer
Abhi Gangani
Developer Advocate
Kshipra Dhame
Developer Advocate
Ready to get started with Fetch.ai Platform?
