Join NIMHD SCHARE Whole-Person Chronic Disease Disparities Challenges

Here is the information from NIH NIMHD(National Institute on Minority Health and Health Disparities):

"The SCHARE (Science Collaborative for Health and Artificial intelligence Reduction of Errors) Whole-Person Chronic Disease Disparities Data Science Grand Challenges comprise three challenges. Together, they address the full AI chronic disease disparities research cycle: measure -> integrate -> model -> predict -> intervene -> measure again. Each challenge can be entered individually and separately, or participants can enter two or all three.

TAKE THE CHALLENGE: Data Integration and Reimagined Computations - Bring the Data Together - Apply and help build the next generation of AI-ready data and computations for whole-person AI-enabled exposome biomedical research.

https://www.nimhd.nih.gov/resources/schare/grand-challenges

BRING YOUR BEST IDEAS: Develop bold new ways to integrate biological, behavioral, environmental, healthcare, and population data into validated, AI-ready whole-person exposome data to better mitigate chronic diseases and reimagine computations to address challenges of chronic disease disparity populations.

REIMAGINE WHAT'S POSSIBLE: Create innovative computational methods and AI-ready analytics that overcome fragmented, missing, sparse-sample-size, multimodal, and multiscale data challenges, include all people, and work across populations.

TURN DATA INTO DISCOVERY: Build scalable, reusable solutions that reveal drivers of chronic disease, accelerate prevention and intervention research, and help reduce chronic disease disparities.

TAKE THE CHALLENGE: Digital Twins and/or Synthetic Data Population Modeling for Whole-Person Health to Mitigate Chronic Disease Disparities - Apply and help build the next generation of AI-ready approaches using synthetic data to enable whole-person/exposome and population health biomedical research.

https://www.nimhd.nih.gov/resources/schare/grand-challenges

BUILD IT: Create scientifically validated digital twins and synthetic populations that model exposome data, including biology, behavior, environment, healthcare, and daily living, representative across populations.

PROVE IT WORKS: Demonstrate that your models are representative, reusable, AI-ready, and fit for purpose in modeling chronic disease risk, progression, and disparities.

PUT IT TO THE TEST: Simulate disease trajectories and interventions to accelerate discovery, identify what works for whom, strengthen precision prevention, and improve individual and population chronic disease health.

TAKE THE CHALLENGE: Smart Devices and Sensing Technologies to Mitigate Chronic Disease Disparities - Apply your best ideas to build and validate the next generation of wearable smart devices and population tools to improve public health and advance whole-person/exposome biomedical research.

https://www.nimhd.nih.gov/resources/schare/grand-challenges

INVENT IT. APPLY NOW: Build affordable, interoperable smart devices and sensing technologies that generate reliable, AI-ready whole-person or population exposome data in real-world settings to mitigate chronic diseases.

DESIGN OR RE-DESIGN IT FOR EVERYONE. TAKE THE CHALLENGE: Create scalable technologies that are accessible and affordable and work across individuals, populations, and environments often missed by today's devices.

TURN SENSING INTO ACTION. SHOW US HOW: Use continuous real-world data to detect chronic disease risk earlier, guide interventions, and improve individual and population health.

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Source and more information: https://www.nimhd.nih.gov/resources/schare/grand-challenges