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  1. Home
  3. Borrowing From The Future: Enhancing Early Risk Assessment Through Contrastive Learning.
  1. Home
  3. Borrowing From The Future: Enhancing Early Risk Assessment Through Contrastive Learning.

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

Minghui Sun1, Matthew M Engelhard1, Benjamin A Goldstein1

  • 1Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.

Proceedings of Machine Learning Research
|November 27, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces Borrowing From the Future (BFF), a new method to improve early pediatric risk assessment. BFF enhances prediction accuracy by leveraging data from later stages to inform earlier assessments.

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Area of Science:

  • Pediatric Health
  • Machine Learning
  • Biomedical Informatics

Background:

  • Pediatric risk assessments are performed across multiple developmental stages, including prenatal, birth, and WellChild visits.
  • While accuracy increases with later-stage predictions, early and reliable risk assessment is clinically crucial.
  • Current methods face challenges in maximizing predictive power during the earliest assessment windows.

Purpose of the Study:

  • To enhance the performance of early-stage risk assessments in pediatric populations.
  • To develop a novel framework that improves prediction accuracy using available data across multiple time points.
  • To enable more reliable clinical decision-making by providing timely risk evaluations.

Main Methods:

  • Introduced Borrowing From the Future (BFF), a contrastive multi-modal framework.
  • Treated each time window (e.g., prenatal, birth, WellChild visits) as a distinct modality.
  • Trained the model on all available data while performing risk assessments using up-to-time information, enabling "borrowing" of signals from future stages to supervise earlier ones.

    • Main Results:

    • Demonstrated consistent improvements in early risk assessment accuracy on two real-world pediatric outcome prediction tasks.
    • Validated the effectiveness of the BFF framework in enhancing predictive performance during initial assessment stages.
    • Showcased the capability of the contrastive approach to implicitly supervise learning at earlier stages using data from later time points.

    Conclusions:

    • The Borrowing From the Future (BFF) framework significantly enhances early pediatric risk assessment accuracy.
    • BFF offers a promising approach for improving clinical decision-making by providing more reliable predictions earlier in a child's development.
    • The study highlights the potential of multi-modal, contrastive learning frameworks in addressing challenges in longitudinal health data analysis.