用真实患者数据训练生成模型，模拟不同治疗下病情发展。
Generating Counterfactual Patient Timelines from Real-World Data
- 基于30万患者数据，用自监督方式训练自回归生成模型。
- 模拟老年、高炎症和肾功能差患者，死亡率与用药变化符合临床规律。
- 适合医学研究者做虚拟临床试验，辅助个性化治疗决策。
反事实模拟——探索在不同临床情景下的假设性后果——在个性化医疗和虚拟临床试验中具有巨大潜力，但受方法学限制仍具挑战。本文展示，一个在超过30万患者及4亿条患者时间线数据上训练的自回归生成模型，可生成临床上合理的反事实轨迹。以2023年新冠住院患者为验证任务，通过修改年龄、血清超敏C反应蛋白（CRP）和血清肌酐水平，模拟7天结局。结果显示，年龄越大、CRP升高、肌酐升高时院内死亡率上升；高CRP下瑞德西韦使用增加，肾功能受损时减少。这些反事实轨迹复现了已知临床规律。结果表明，基于真实世界数据以自监督方式训练的自回归生成模型，可为反事实临床模拟奠定基础。
原文摘要 · Abstract (English)
Counterfactual simulation - exploring hypothetical consequences under alternative clinical scenarios - holds promise for transformative applications such as personalized medicine and in silico trials. However, it remains challenging due to methodological limitations. Here, we show that an autoregressive generative model trained on real-world data from over 300,000 patients and 400 million patient timeline entries can generate clinically plausible counterfactual trajectories. As a validation task, we applied the model to patients hospitalized with COVID-19 in 2023, modifying age, serum C-reactive protein (CRP), and serum creatinine to simulate 7-day outcomes. Increased in-hospital mortality was observed in counterfactual simulations with older age, elevated CRP, and elevated serum creatinine. Remdesivir prescriptions increased in simulations with higher CRP values and decreased in those with impaired kidney function. These counterfactual trajectories reproduced known clinical patterns. These findings suggest that autoregressive generative models trained on real-world data in a self-supervised manner can establish a foundation for counterfactual clinical simulation.
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