🤖 AI Summary
This work addresses the significant challenge of simulating clinical patient trajectories, which are shaped by complex biological and social factors, thereby hindering advances in personalized medicine and virtual clinical trials. To this end, we leverage over 200 million real-world electronic health records to develop the first large-scale, pre-trained generative simulator capable of modeling the probabilistic distribution of future clinical events, laboratory results, and their temporal dynamics based solely on a patient’s historical data. The generated trajectories exhibit high fidelity to real-world observations, with incidence rates, lab values, and temporal patterns closely matching empirical data. Notably, the observed-to-expected ratios for diverse clinical outcomes consistently approximate 1.0, demonstrating the model’s effectiveness and potential for high-fidelity patient trajectory simulation.
📝 Abstract
Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model patient timelines. We developed a generative simulator model that takes a patient's history as input and synthesizes fine-grained, realistic future trajectories. The model was pretrained on more than 200 million clinical records. It produced high-fidelity future timelines, closely matching event occurrence rates, laboratory test results, and temporal dynamics in real patient future data. It also accurately estimated future event probabilities, with observed-to-expected ratios consistently near 1.0 across diverse outcomes and time horizons. Our results reveal the untapped value of real-world data in electronic health records and introduce a scalable framework for in silico modeling of clinical care.