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Virtual Hackathon &

In-Person Co-Working & Demo Day in San Francisco

Purpose

Welcome to Research to the People and Stanford Medicine's Rare Disease AI Hackathon. The purpose of this event series is bringing AI and medical experts together to build open source language models for rare diseases, and create zero-barrier access to rare disease expertise for patients, researchers and physicians. We're excited for the potential AI has to uncover new biomarkers, treatment ideas and novel links between rare diseases. As models are built and tested, this event will also explore validation methods for medical AI. Successful models will be released for further testing and validation on Hypophosphatasia.ai and EhlersDanlos.ai.

 

Vision

Our vision is to jumpstart an ongoing open source community for rare disease AI models.

 

Timeline 

  • April 2024 Event announced!

  • Asynchronous: Teams work on models. Office hours sessions on training, fine tuning and validating models: Virtual.

  • TBD: Learn about Ehlers-Danlos Syndrome. Online.

  • TBD: Learn about Hypophosphatasia. Online.

  • TBD: In-person co-working and office hours hosted by Fifty Years

  • TBD: In-person co-working and office hours hosted at Research to the People Loft. 

  • June 17th at 12.00am: Final results due.

  • June 21st 2024: In-Person Demo Day in San Francisco! Open to the public.

  • Ongoing: Models released for Beta testing to broader patient and physician community by Research to the People. Teams will be invited to publish their work in the Research to the People Journal.

 

Rare Disease Focus

  1. Hypophosphatasia.

  2. Ehlers Danlos Syndrome.

 

​Datasets

Our baseline dataset are publicly available PubMed papers for HPP and EDS. Additional datasets and data sources in the works. *Teams are encouraged to use additional datasets outside of what we provide.* Special thanks to teams who are helping source new datasets to make available for this event! Data downloads available for onboarded teams. 

  1. Hypophosphatasia - 814 publications in machine readable format.

  2. Ehlers Danlos Syndrome. - 1662 publications in machine readable format.

Problem Tracks

  1. Search & Recall: Create a custom model chat interface for HPP and EDS scientific publications.

  2. Novel Discovery: Use additional bio datasets to create a custom model that can augment research for HPP and EDS, ie: new biomarkers.

Hackathon Events

The bulk of this hackathon will take place virtually, with teams interfacing with AI Researchers, Patients, and Medical experts through virtual and in-person (SF Bay Area) events. The hackathon will focus on fine tune training. The demo day will focus on collective interrogation of the models, especially via doctors, patients and medical experts.

 

Format

We're doing a rolling start, and welcoming teams to start building models ASAP. Teams can signup and access the data right away to start training their models. Virtual and in-person hackathon events and office hours will be hosted by our team and leading experts leading up to the demo day: June 21st 2024.

 

Over the course of these events, teams will focus on fine-tuning models with AI and medical experts. This work will culminate in a live demo day pitch session with patients, medical experts, investors and the broader AI and Rare Disease Community. ​

Demo Day

Our Demo Day will be open to the public. Teams will present live demos of their models and lead a discussion on their approach. This event will feature VC's and investors who are funding generative AI in healthcare and medicine.​ Note: Models developed for this Hackathon are expected to be open-source. Teams will be able to connect with VC's and Investors for other generative AI projects.

Recommended Tools

  1. Llama 2

  2. Run Llama locally with GG.ML

  3. HuggingFace

  4. LangChain

  5. ChromaDB

  6. Pinecone

  7. Ray

Recommended Reading

  1. The shaky foundations of large language models and foundation models for electronic health records.

  2. Challenges and Applications of Large Language Models.

  3. LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers

Purpose

Welcome to Research to the People and Stanford Medicine's Rare Disease AI Hackathon. The purpose of this event series is bringing AI and medical experts together to build open source language models for rare diseases, and create zero-barrier access to rare disease expertise for patients, researchers and physicians. We're excited for the potential AI has to uncover new biomarkers, treatment ideas and novel links between rare diseases. As models are built and tested, this event will also explore validation methods for medical AI. Successful models will be released for further testing and validation  on 

Hypophosphatasia.ai and EhlersDanlos.ai.

 

Vision

Our vision is to jumpstart an ongoing open source community for rare disease AI models.

 

Timeline 

Note: Exact dates are still being finalized.

  • March 2024 Event Announced and datasets released.

  • Month/Date/Year: Kick-off event. Teams work on models. Office hours sessions on training, fine tuning and validating models. Virtual and In-person.

  • Month/Date/Year: In-person co-working and office hours hosted by Fifty Years

  • Month/Date/Year: In-person co-working and office hours hosted at TBD. 

  • June 21st 2024: In-Person Demo Day in San Francisco! Open to the public.

  • Ongoing: Models released for Beta testing to broader patient and physician community by Research to the People. Teams will be invited to publish their work in the Research to the People Journal.

 

Rare Disease Focus

  1. Hypophosphatasia.

  2. Ehlers Danlos Syndrome.

 

​Dataset

Clinical Guidelines and PubMed publications for HPP and EDS. Additional datasets are expected to become available. Data downloads available for onboarded teams.

  1. Hypophosphatasia - 814 publications in machine readable format.

  2. Ehlers Danlos Syndrome - 216 publications in machine readable format.

Problem

  1. Create custom models for Hypophosphatasia and Ehlers Danlos Syndrome.

  2. Advanced: Interface the models to create novel research connections between HPP and EDS.

Hackathon Events

The bulk of this hackathon will take place virtually, with teams interfacing with AI Researchers, Patients, and Medical experts through in-person (SF Bay Area) and virtual sessions. This hackathon will focus on:

  1. Fine tune training.

  2. Doctors, Patients and Medical Experts interrogating models.

  3. Advanced: Developing methods for models to interface with each other, creating the possibility of new research links between HPP and EDS.

 

Format

We're doing a rolling start, and welcoming teams to start building models ASAP. Teams can signup and access the data right away to start training their models. Virtual and in-person hackathon events and office hours will be hosted by our team and leading experts leading up to the demo day: June 21st 2024.

 

Over the course of these events, teams will focus on fine-tuning models with AI and medical experts. This work will culminate in a live demo day pitch session with patients, medical experts, investors and the broader AI and Rare Disease Community. ​

Demo Day

Our Demo Day will be open to the public. Teams will present live demos of their models and lead a discussion on their approach. This event will feature VC's and investors who are funding generative AI in healthcare and medicine.​ Note: Models developed for this Hackathon are expected to be open-source. Teams will be able to connect with VC's and Investors for other generative AI projects.

Recommended Tools

  1. Llama 2

  2. Run Llama locally with GG.ML

  3. HuggingFace

  4. LangChain

  5. ChromaDB

  6. Pinecone

  7. Ray

Recommended Reading

  1. The shaky foundations of large language models and foundation models for electronic health records.

  2. Challenges and Applications of Large Language Models.

  3. LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers

Pete Kane

Organizer

Stanford Medicine & Founder of Research to the People.

Stanley Bishop

Organizer

Friendly Neighborhood AI-Scientist. Ex Google.

Jessy Reyes

Organizer

Surgical Tech

Stanford Medicine

Jacob Cole

Organizer

Founder, IdeaFlow.

MIT Alum.

Majo Durán

Organizer

Bio Fellow at Fifty Years.

USCF Immunotherapy Scientist.

Nandita Damaraju

Organizer

ML Engineer, Inflammatix.

Georgia Tech Alum.

Organizers

Patient Collaborators

Gigi

Patient Collaborator

HPP & EDS Patient. Grant Writer & RTTP Collaborator.

John

Patient Collaborator

Patient Advisor. Founder, BioHackers Guild. 

Undiagnosed-1 Patient.

Sue

Patient Collaborator

Hypophosphatasia Patient Collaborator.

Sam

Patient Collaborator

Citizen Scientist Systems Biologist Patient-Turned-Researcher.

More Coming

Michael Snyder, PhD

Advisor

Chair, Stanford Medicine, Genetics and Personalized Medicine.

Lara Bloom

Mentor

President & CEO of The Ehlers-Danlos Society.

Fanny Sie

Mentor

Head of AI & Emerging Technology Collaboration   Roche Global Informatics

Katie Link

Mentor

ML Engineer, Hugging Face, ML Research Engineer at NYU Langone Health.

Dr. Woody Gandy

Mentor

Internal Medicine, Board of Directors, Ehlers-Danlos Society.

Bharath Ramsundar, PhD

Mentor

Founder, Deep Forest and Deep Chem. O’Reilly Author. UC Berkeley & Stanford Alum.

David Hall, PhD

Mentor

Research Engineering Lead at
Stanford HAI. Ex. Microsoft. UC Berkeley & Stanford Alum.

Avantika Lal, PhD

Mentor

Principal AI Scientist at Genentech (Roche)

Ex. Insitro, NVIDIA, Stanford Alum.

Liezl Puzon

Mentor

Founder, AI Validation Startup

Ex Facebook NLP Engineer.

Chloe Hsu, PhD

Mentor

ML Scientist.

Ex: Google, Meta,

UC Berkeley, Cal Tech Alum.

More Coming

Patient Collaborator

Medical & AI Experts

Additional Model Evaluators

RonJon Nag

Evaluator

Inventor, teacher and entrepreneur. Adjunct Professor at Stanford University.

More Coming

Backend Engineering

Helping the hackathon and demo day run smoothly!

Rob

Engineering

Founder, Infinite.Tech

Jan Zheng

Engineering

co-Founder, Phage.Direectory

Arzav Jain

Engineering Advisor

Engineer, OpenAI

Stanford University

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