Data Sphere

The purpose of this project is to leverage Generative AI (GenAI) technologies to transform and standardize diverse clinical trial datasets from Project Data Sphere. By converting heterogeneous data sources into a unified, interoperable data model, the initiative aims to facilitate more efficient data harmonization and enable seamless cross-trial analysis. This transformation will support more rapid, data-driven insights and decision-making in clinical research, ultimately fostering scalable and consistent methodologies within the life sciences industry. Through this innovative approach, the project seeks to enhance the value of clinical data, accelerate research timelines, and improve the robustness of clinical trial outcomes.

Transforming Clinical Data with GenAI: This initiative explores the potential of Generative AI (GenAI) technologies to revolutionize how clinical trial data from Project Data Sphere is utilized. By transforming diverse datasets from different trial sponsors into a unified, standard data model, the project aims to streamline data harmonization and enable seamless cross-trial analyses. This would not only enhance efficiency but also open the door for advanced insights and data-driven decision-making in clinical research, paving the way for consistent and scalable methodologies across the life sciences industry.

Data has been anonymized in accordance with applicable compliance requirements, such as HIPAA, UCI, and other relevant privacy standards, ensuring that no personally identifiable information (PII) remains accessible.

Tools and Skills Students will Use and Learn : Implementation of LLMs and prompt engineering to synthesize data from different sponsors

Preference for Student Profile: Conceptual understanding of GenAI frameworks

Proficiency in python or R