Linjie Mu, Zhongzhen Huang, Yannian Gu, Shengqian Qin, Shaoting Zhang, Xiaofan Zhang
EHRWorld is a new medical world model that improves long-term clinical simulations by using a large dataset of real-world electronic health records, outperforming models based solely on large language models (LLMs).
In the medical field, predicting how a patient's condition will change over time and how they will respond to treatments is crucial but challenging. Traditional large language models (LLMs) have been good at reasoning about medical issues in a static way, but they struggle to accurately simulate the progression of diseases and the effects of treatments over time. To address this, researchers have developed EHRWorld, a new model that uses a large dataset of electronic health records to better predict patient outcomes. This new approach offers more reliable and stable simulations, which could lead to better healthcare decision-making.
