用真实病历数据生成高保真患者未来健康轨迹
High-Fidelity Longitudinal Patient Simulation Using Real-World Data
- 基于2亿+真实病历训练生成模型，输入病史即可预测未来病情
- 生成结果在事件发生率、检验值和时间动态上接近真实数据
- 适合临床研究、个性化治疗设计，尤其关注虚拟试验的团队
模拟是探索不确定性的有力工具，在临床医学中具有变革潜力，可用于个性化治疗规划和虚拟临床试验。然而，由于复杂的生物与社会文化因素影响，模拟患者轨迹极具挑战。本文展示可通过真实世界临床记录实证建模患者时间线。我们开发了一种生成式模拟模型，以患者病史为输入，合成精细且逼真的未来轨迹。模型在超过2亿条临床记录上预训练，生成的未来时间线具有高保真度，其事件发生率、实验室检查结果及时间动态均与真实患者数据高度一致。模型对未来事件概率的估计准确，不同结局与时间范围下观察值与期望值比值始终接近1.0。结果揭示了电子健康记录中未被发掘的现实数据价值，并提出一种可扩展的临床照护数字建模框架。
原文摘要 · Abstract (English)
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.
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