Abstract
The analysis and prediction of Electronic Health Records (EHR) are essential components of modern healthcare systems, providing significant benefits to both providers and patients. With the increasing availability of vast amounts of patient data in EHR systems, these resources offer both challenges and opportunities for predictive modeling. Reinforcement Learning (RL) has gained popularity in EHR research, particularly due to its strength in handling sequential data, making it well-suited for time series analysis and prediction in healthcare. However, many existing approaches simplify disease progression by modeling it as a Markovian process, potentially overlooking the complex influence of a patient's medical history on disease development. In this study, we propose leveraging Large Language Models (LLMs) to encode patients' disease-treatment histories, enhancing the representation of historical data. We then employ reinforcement learning techniques to optimize treatment recommendations. Furthermore, we aim to develop an auxiliary LLM model independently trained to predict future disease occurrences, thereby improving the robustness and accuracy of our predictive framework.
