用大模型构建眼病患者数字孪生，精准预测视力变化轨迹。
OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology
- 基于LLM将3220名患者病史转为结构化叙事，生成个性化视力预测。
- 在新生血管性黄斑变性中，预测误差比基线低6.0%。
- 无需填补缺失数据，适合复杂多变的长期病程建模，适合临床研究使用。
眼科精准医疗需要准确的纵向预测，但多模态临床数据碎片化阻碍了发展。我们提出OphthaDT，一个基于大语言模型的眼科数字孪生系统，将来自四项Ⅲ期临床试验的3220名患者的纵向病史转化为结构化叙述，用于预测最佳矫正视力（BCVA）。在长达100周的基准测试中，OphthaDT在新生血管性年龄相关性黄斑变性（nAMD）中的预测误差最低，平均绝对误差（MAE）较所有基线降低6.0%。在糖尿病性黄斑水肿（DME）中，性能与各基线相当，且相比随机森林和XGBoost分别降低2.6%和6.9%的平均MAE。结果表明，当病程越复杂时，OphthaDT的优势越明显：线性模型对较稳定的DME治疗反应仍有效，而其高阶建模能力更适用于nAMD的高变异性轨迹。此外，OphthaDT无需插补即可处理不规则采样数据，为减少患者负担、加速药物研发提供了新方法。
原文摘要 · Abstract (English)
Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a barrier to forecasting. We introduce OphthaDT, an LLM-based digital twin for ophthalmology that serializes longitudinal patient histories from 3,220 patients across four Phase III clinical trials into structured narratives to forecast best corrected visual acuity (BCVA). In benchmarks spanning up to 100 weeks, OphthaDT demonstrated the lowest prediction error in neovascular age-related macular degeneration (nAMD), achieving an average mean absolute error (MAE) reduction of 6.0% compared to all baselines. In diabetic macular edema (DME), OphthaDT demonstrated competitive performance against all baselines while outperforming Random Forest and XGBoost by an average MAE reduction of 2.6% and 6.9%, respectively. Results reveal that OphthaDT's predictive advantage scales with trajectory complexity: whereas linear models remain effective for the more stable treatment responses of DME, OphthaDT's capacity is better suited for capturing the high longitudinal variability of nAMD. Finally, OphthaDT handles irregular sampling without imputation, positioning LLM-based clinical trajectory modeling as a methodology that could reduce patient burden and accelerate drug development.
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