用后期数据提升早期儿童风险预测准确率
Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning
- 通过对比学习让早期模型‘借’到后期数据的有用信号
- 在两项真实儿科预测任务中，早期阶段表现显著提升
- 适合需要早筛的医疗场景，如产前和新生儿评估
针对儿科人群的风险评估通常分多个阶段进行，如产前、出生时及常规儿童保健随访。尽管后期预测精度更高，但临床更希望尽早做出可靠判断。本文提出一种名为Borrowing From the Future（BFF）的对比多模态框架，将每个时间窗口视为独立模态。该模型在全时间段数据上训练，但在预测时仅使用当前及之前的信息。通过对比学习机制，模型可隐式利用后期信息（如儿童保健随访）来指导早期阶段（如产前/出生时）的学习。在两个真实儿科结果预测任务上验证了BFF的有效性，均实现了早期风险评估性能的持续提升。代码已开源：https://github.com/scotsun/bff。
原文摘要 · Abstract (English)
Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visits. Although predictions made at later stages typically achieve higher precision, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on improving prediction performance in early-stage risk assessments. Our solution, \textbf{Borrowing From the Future (BFF)}, is a contrastive multi-modal framework that treats each time window as a distinct modality. In BFF, a model is trained on all available data throughout the time while performing a risk assessment using up-to-date information. This contrastive framework allows the model to ``borrow'' informative signals from later stages (e.g., Well-Child visits) to implicitly supervise the learning at earlier stages (e.g., prenatal/birth stages). We validate BFF on two real-world pediatric outcome prediction tasks, demonstrating consistent improvements in early risk assessments. The code is available at https://github.com/scotsun/bff.
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