This monorepo contains a set of:
The current system of clinical research, diagnosis, and treatment is failing the billions of people are suffering from chronic diseases.
๐ Problems we're trying to fix...
By harnessing global collective intelligence and oceans of real-world data, we hope to emulate Wikipedia's speed of knowledge generation.
This is a very high-level overview of the architecture. The three primary primitive components of the FDAi framework are:
The core characteristics that define the FDAi are:
FDAi Gateway API Nodes should make it easy for data silos, such as hospitals and digital health apps, to let people export and save their data locally in their PersonalFDA Nodes.
๐ Learn More About Gateway APIs
PersonalFDA Nodes are applications that can run on your phone or computer. They import, store, and analyze your data to identify how various factors affect your health. They can also be used to share anonymous analytical results with the Clinipedia FDAi Wiki in a secure and privacy-preserving manner.
PersonalFDA Nodes are composed of two components, a Digital Twin Safe and a personal AI agent applies causal inference algorithms to estimate how various factors affect your health.
aider
A local application for self-sovereign import and storage of personal data.
๐Learn More or Contribute to Digital Twin Safe
Personal AI agents that live in your PersonalFDA nodes and use causal inference to estimate how various factors affect your health.
๐Learn More About Optimitron
The Clinipedia wiki should be a global knowledge repository containing the aggregate of all available data on the effects of every food, drug, supplement, and medical intervention on human health.
๐ Learn More or Contribute to the Clinipedia
A key component of Clinipedia is Outcome Labels that list the degree to which the product is likely to improve or worsen specific health outcomes or symptoms.
๐ Learn More About Outcome Labels
A collective intelligence coordination platform is needed for facilitating cooperation, communication, and collaborative actions among contributors.
๐ Learn More or Contribute to the FDAi Collaboration Framework
We'd love your help and input in determining an optimal roadmap for this project.
๐ Click Here for a Detailed Roadmap
Our goal is to develop FAIR (Findable, Accessible, Interoperable, and Reusable) data and analytical tools that can be used by any regulatory agencies, businesses, non-profits or individuals to quantify the effects of every food, drug, supplement, and treatment on every measurable aspect of human health and happiness.
The Nx Monorepo is to achieve maximum interoperability and minimum duplication of effort between the various projects to maximize the speed of development and minimize costs. This can be done by modularizing the codebase into libraries and plugins that can be shared between the various projects.
Apps in this monorepo:
We've got a monolithic centralized implementation of the FDAi at apps/dfda-1 that we're wanting to modularize and decentralize into a set of FAIR libraries and plugins that can be shared with other apps.
Currently, the main apps are the Demo Data Collection, Import, and Analysis App and the Journal of Citizen Science.
 
Collects and aggregate data on symptoms, diet, sleep, exercise, weather, medication, and anything else from dozens of life-tracking apps and devices. Analyzes data to reveal hidden factors exacerbating or improving symptoms of chronic illness.
Web and mobile push notifications with action buttons.
By using the Browser Extension, you can track your mood, symptoms, or any outcome you want to optimize in a fraction of a second using a unique popup interface.
The Analytics Engine performs temporal precedence accounting, longitudinal data aggregation, erroneous data filtering, unit conversions, ingredient tagging, and variable grouping to quantify correlations between symptoms, treatments, and other factors.
It then pairs every combination of variables and identifies likely causal relationships using correlation mining algorithms in conjunction with a pharmacokinetic model. The algorithms first identify the onset delay and duration of action for each hypothetical factor. It then identifies the optimal daily values for each factor.
๐ More info about data analysis
More info about outcome labels
More info about real time decision support
๐ More info about the predictor search engine...
๐ More info about observational studies...
๐ค Join Us: Whether you're a developer, researcher, health professional, regulatory, or simply passionate about health innovation, your contribution can make a monumental difference!
๐ Tell Us About Your Project!
Code or documentation improvements are eternally appreciated!
It's our goal to avoid any duplication of effort. So please include existing projects that would be interested in fulfilling any part of this global framework.
If you have any questions or need help, please create an issue instead of emailing us so that others can benefit from the discussion.