Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning

2025-08-15

AI Summary

To address suboptimal predictive performance in pediatric early risk assessment caused by data sparsity, this paper proposes a contrastive learning–based multimodal temporal transfer framework. Methodologically, it treats distinct clinical time windows as independent modalities and leverages later-stage rich information to enhance early prediction via cross-window knowledge transfer, implemented through contrastive learning; temporal alignment and joint multimodal training further improve representation consistency. Evaluated on two real-world pediatric tasks—early sepsis warning and acute kidney injury prediction—the framework significantly improves AUC and recall at critical early time points (e.g., 1–3 hours post-admission), demonstrating both effectiveness and generalizability. This work establishes a novel paradigm for low-resource time-series forecasting in clinical settings.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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, extbf{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.

Problem

• Improving early-stage pediatric risk assessment accuracy
• Enhancing predictions using contrastive multi-modal learning
• Borrowing later-stage data to supervise earlier-stage learning

Innovation

• Contrastive multi-modal framework for early risk
• Borrows signals from later stages implicitly
• Improves pediatric outcome prediction performance