自动提取生存曲线并生成带不良事件的合成患者数据，填补临床研究数据共享空白。
KMGen: A Skill-based Approach for Synthetic Individual Patient Data Generation
- 用智能代理自动化提取生存曲线，无需人工干预
- 生成符合真实生存分布和不良事件模式的合成患者数据，误差低于5.1%
- 适合需隐私保护数据的医药研发与统计建模人员使用
临床试验中的个体患者数据（IPD）是生存分析、荟萃分析和安全性研究的基础，但极少公开。现有工作仅解决了一半问题：从发表图中重建生存曲线（通常需手动数字化或人工修正），却无法生成构成患者记录另一半的不良事件流。本文提出首个端到端框架KMGen，(i) 实现与人工引导工具相当精度的全自动生存曲线提取，(ii) 从公开注册信息生成合成的逐患者不良事件轨迹。提取阶段为全自动化智能体流水线——代理自动生成代码逐步提取曲线，32个样本基准测试下平均积分绝对误差（IAE）为0.0151，覆盖清晰、边缘及对抗性场景。生成阶段将患者原型提取与统计采样解耦：大模型提炼试验记录为分组特异性统计量、不良事件、人口学特征与风险倍数；机制化采样器通过临床原型生成事件，利用经验生存曲线进行秩相关耦合（精确保留边际生存分布），并采用基于周期的事件调度策略（区分诱导与维持期）。在三个覆盖不同规模的肿瘤学试验上，经三轮独立重生成，平均整合生存曲线绝对差Δ_KM ≤ 0.051，性别/ECOG评分的JS散度≤0.013（6个变量中5个达标），且在单一固定参数下恢复≥71%的前15位不良事件（按确切MedDRA术语计）。该流程已开源，地址：https://github.com/chufangao/kmgen。
原文摘要 · Abstract (English)
Individual patient data (IPD) from clinical trials is the substrate for survival modeling, meta-analysis, and safety research, yet IPD is rarely released. Prior work has addressed only half of this gap: reconstructing Kaplan-Meier (KM) curves from published plots -- typically requiring manual digitization or human-in-the-loop correction -- while offering no mechanism for generating the adverse-event (AE) streams that constitute the other half of a patient record. We introduce KMGen, the first end-to-end framework that (i) fully automates KM curve extraction at accuracy competitive with human-guided tools, and (ii) generates synthetic per-patient AE trajectories from public trial registry records. The extraction stage is a fully automated agentic pipeline -- an agent generates code to extract each step in the KM curve -- achieving a mean Integrated Absolute Error (IAE) of 0.0151 on a 32-plot benchmark spanning clean, edge-case, and adversarial conditions. The IPD generation stage decouples patient archetype extraction from statistical sampling: an LLM distills the trial record into arm-specific statistics, adverse events, patient demographics, and risk multipliers. A mechanistic sampler generates patient events via clinical archetypes, bootstrap rank-correlation coupling to the empirical KM curve (preserving the marginal survival distribution exactly), and cycle-based AE scheduling with an induction/maintenance split. Across three held-out oncology trials spanning an order of magnitude in cohort size and 30 independent regenerations per trial, KMGen achieves mean integrated KM absolute difference $Δ_{\text{KM}}\,{\leq}\,0.051$, sex/ECOG JSD ${\leq}\,0.013$ on 5 of 6 demographic slots, and recovers ${\geq}\,71\%$ of the top-15 AEs by exact MedDRA term under a single fixed parameter set. The pipeline is released as open source at https://github.com/chufangao/kmgen.
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