Machine Learning and Data Science Sponsored by HRP

Machine Learning and Data Science

Sponsored by NASA Human Research Program ( HRP )

Purpose: This project represents a crucial foundational step in developing advanced medical machine learning and AI systems. As with all AI and machine learning initiatives, the key challenge lies in sourcing reliable training and testing data. This project specifically targets these needs while also exploring the creation of a potential simulator that can aggregate or generate diverse datasets. This approach serves as a stopgap measure to address data gaps and facilitate the ongoing development of machine learning systems and programs.

Problem Statement: Artificial intelligence (AI) and machine learning programs rely heavily on high-quality data. As with any data acquisition effort, researchers must validate the sources of their data and assign quality metrics based on both the source and the data itself. For instance, a simple rating system could be employed, where sources are rated from A to C—A being the highest quality and C the lowest—while the actual data is rated on a scale from 1 to 10, with 10 representing the highest quality. This data must be sourced from reputable research institutions or standardized groups, both nationally and internationally. This rigorous validation process enables researchers to trust and test each piece of data, ensuring the integrity of their data collections.