用图模型分析血检数据，找出疾病发作的关键时间点。
Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers
- 将血检时间点建模为带时差的有向图，捕捉动态变化规律。
- 精准识别与疾病相关的关键时间点，准确率优于传统插补方法。
- 结果可解释性强，适合临床医生辅助诊断和生物机制研究。
常规临床血检是大规模生物医学研究的新兴分子数据源，但存在采样不规则和信息缺失问题。传统方法依赖插补，会扭曲学习信号并引入偏差，且缺乏生物学可解释性。本文提出一种基于图神经加法网络（GNAN）的新方法，将生物标志物轨迹建模为时间加权的有向图，节点代表采样事件，边编码事件间的时间间隔。GNAN的加法结构可显式分解特征与时间的贡献，实现对关键疾病关联时间点的检测。相比传统插补方法，该模型在不破坏稀疏数据时间结构的前提下，避免了人为偏差，并通过分解各生物标志物与时间区间的贡献，提供内在可解释的预测结果，具备临床应用价值，能发现具有生物学意义的疾病特征。
原文摘要 · Abstract (English)
Routinely collected clinical blood tests are an emerging molecular data source for large-scale biomedical research but inherently feature irregular sampling and informative observation. Traditional approaches rely on imputation, which can distort learning signals and bias predictions while lacking biological interpretability. We propose a novel methodology using Graph Neural Additive Networks (GNAN) to model biomarker trajectories as time-weighted directed graphs, where nodes represent sampling events and edges encode the time delta between events. GNAN's additive structure enables the explicit decomposition of feature and temporal contributions, allowing the detection of critical disease-associated time points. Unlike conventional imputation-based approaches, our method preserves the temporal structure of sparse data without introducing artificial biases and provides inherently interpretable predictions by decomposing contributions from each biomarker and time interval. This makes our model clinically applicable, as well as allowing it to discover biologically meaningful disease signatures.
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