Transforming NIH HEAL Initiative Clinical Trial Data Using Machine Learning/Artificial Intelligence

The HEAL Data Ecosystem is working to collect data across its projects and networks to meet FAIR (Findable, Accessible, Interoperable, Reusable) data standards. This administrative supplement builds on the mission of the NIH HEAL IMPOWR network to blend existing and future chronic pain (CP) and opioid use disorder (OUD) data. The proposed work will significantly deepen and augment approaches to FAIR principles in CP and OUD data for both the HEAL network and larger NIH research community. The overall objective of this project is to move CP and OUD data one step closer to FAIR by leveraging existing datasets and developing tools for new projects. The general hypothesis of the project is that leveraging existing CP & OUD data and collecting new data using ML/AI data quality standards will accelerate the impact of the HEAL Data Ecosystem. The aims of the project are to transform existing datasets to be ML/AI ready, and to adapt tools to support ML/AI readiness for existing and prospectively collected HEAL CDE. The expected outcome of this project is data optimization pipelines and tools to support the goal of ML/AI ready data. The results of this project will provide a strong basis for further development of the HEAL Data Ecosystem, helping to bring diverse data sources together and meet FAIR data standards.