An Agent Based Approach to Healthcare Modelling with Big Data and AI

This project explores the integration of agent-based models (ABMs) with large language models (LLMs) and big data in healthcare simulations. ABMs are powerful tools for representing complex systems, such as economic and healthcare environments, by simulating the interactions of autonomous agents. By embedding LLMs like Llama 3.1 into these models, the project aims to enhance the decision-making and reasoning abilities of agents, enabling dynamic policy analysis of healthcare systems. The model will use big data sources, such as electronic health records and NHS spending data, to explore policy changes, including healthcare funding reallocations and doctor compensation adjustments. You can find further information on GitHub.

supervisors:

Charles Rahal, Associate Professor in Data Science and Informatics (primary; University of Oxford)