Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning
This work addresses the need for reliable early risk predictions in pediatric healthcare, which is clinically desirable but often less precise than later assessments, representing an incremental advancement in domain-specific methods.
The study tackled the problem of improving early-stage risk assessment in pediatric populations by proposing Borrowing From the Future (BFF), a contrastive multi-modal framework that uses later-stage data to supervise earlier predictions, resulting in consistent improvements in early risk assessments.
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.