用儿童肾病电子病历预测30天内肌酐异常，探索时序建模新方法。
Towards Multimodal Representation Learning in Paediatric Kidney Disease
- 结合实验室数据与人口统计信息，用循环神经网络建模时间序列
- 模型在30天内预测肌酐异常的性能验证了时序模式的有效性
- 为未来多模态临床信号融合提供基础，适合儿科医疗AI研究者参考
儿童肾病表现和进展差异大，需持续监测肾功能。基于2019至2025年英国大奥蒙德街儿童医院收集的电子健康记录，本研究探索了一种整合纵向实验室序列与人口统计信息的时序建模方法。采用循环神经网络对这些数据进行训练，以预测患儿未来30天内是否会出现异常血清肌酐值。作为一项试点研究，该工作初步证明简单时序表示可在常规儿科数据中捕捉有效模式，并为未来利用更多临床信号和更详细的肾功能结局进行多模态扩展奠定基础。
原文摘要 · Abstract (English)
Paediatric kidney disease varies widely in its presentation and progression, which calls for continuous monitoring of renal function. Using electronic health records collected between 2019 and 2025 at Great Ormond Street Hospital, a leading UK paediatric hospital, we explored a temporal modelling approach that integrates longitudinal laboratory sequences with demographic information. A recurrent neural model trained on these data was used to predict whether a child would record an abnormal serum creatinine value within the following thirty days. Framed as a pilot study, this work provides an initial demonstration that simple temporal representations can capture useful patterns in routine paediatric data and lays the groundwork for future multimodal extensions using additional clinical signals and more detailed renal outcomes.
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