用多核方法分析新冠胸片，预测患者是否需进重症监护。
Kernel-Based Learning of Chest X-ray Images for Predicting ICU Escalation among COVID-19 Patients
- 结合多种核函数构建复合模型，捕捉医学影像复杂特征
- 在新冠胸片数据上准确预测重症监护升级，效果优于传统方法
- 适合医疗影像分析与临床决策支持场景
核方法在机器学习中广泛用于分类与预测，因其能捕捉复杂的非线性数据模式。然而，单一核方法受限于仅使用一种核函数（如高斯核），难以充分表达真实数据的异质性。多核学习（MKL）通过组合多个基础核函数，整合异源信息以克服此局限。本文将MKL扩展至适用于指数族分布的输出变量，涵盖更广泛的数据类型，提出广义线性模型集成多加性核回归（GLIMARK）。实验证明，该方法可有效恢复或逼近真实数据生成机制。将其应用于新冠胸片数据集，成功预测患者是否需转入重症监护室，并提取出具有临床意义的特征，凸显其在实际医疗场景中的应用价值。
原文摘要 · Abstract (English)
Kernel methods have been extensively utilized in machine learning for classification and prediction tasks due to their ability to capture complex non-linear data patterns. However, single kernel approaches are inherently limited, as they rely on a single type of kernel function (e.g., Gaussian kernel), which may be insufficient to fully represent the heterogeneity or multifaceted nature of real-world data. Multiple kernel learning (MKL) addresses these limitations by constructing composite kernels from simpler ones and integrating information from heterogeneous sources. Despite these advances, traditional MKL methods are primarily designed for continuous outcomes. We extend MKL to accommodate the outcome variable belonging to the exponential family, representing a broader variety of data types, and refer to our proposed method as generalized linear models with integrated multiple additive regression with kernels (GLIMARK). Empirically, we demonstrate that GLIMARK can effectively recover or approximate the true data-generating mechanism. We have applied it to a COVID-19 chest X-ray dataset, predicting binary outcomes of ICU escalation and extracting clinically meaningful features, underscoring the practical utility of this approach in real-world scenarios.
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