EGS-Net: a knowledge-augmented machine learning framework for predicting future high-myopia risk from longitudinal school-screening trajectories - Takeaways - MDSpire

EGS-Net: a knowledge-augmented machine learning framework for predicting future high-myopia risk from longitudinal school-screening trajectories

  • By

  • Zhan Tang

  • Na Zhao

  • Zhaoyu Huang

  • Jinhao Lu

  • Chao Dai

  • Jian Wang

  • Runze Zheng

  • June 25, 2026

  • 0 min

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  • 1

    The EGS predictive framework utilizes longitudinal school screening data to enhance early risk stratification for high myopia.

  • 2

    The model integrates multi-model ensemble learning with a clinical risk-heuristic override to improve prediction accuracy.

  • 3

    EGS-Net achieved high Recall (0.9533) and Precision (0.9211), enabling reliable identification of future high-myopia risk.

  • 4

    SHAP analysis confirmed the importance of longitudinal trajectory features for model interpretability and transparency.

  • 5

    The study addresses gaps in existing models by leveraging individualized kinetic signals and integrating clinical domain knowledge.

Original Source(s)

EGS-Net: a knowledge-augmented machine learning framework for predicting future high-myopia risk from longitudinal school-screening trajectories

  • by Zhan Tang, Na Zhao, Zhaoyu Huang, Jinhao Lu, Chao Dai, Jian Wang, Runze Zheng

  • June 25, 2026

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