用迁移学习提升重症患者诊断特异性死亡预测准确率
Diagnosis-based mortality prediction for intensive care unit patients via transfer learning
- 基于GLM与XGBoost的迁移学习方法，针对不同诊断定制预测模型
- 在eICU数据集上超越单一诊断训练模型和APACHE IVa评分，校准性更优
- 推荐使用Youden最优截断点而非0.5，适配临床决策需求
重症监护中，不同诊断对应的危重病因差异显著，但针对诊断异质性的预测模型尚未系统研究。本文评估了用于诊断特异性死亡预测的迁移学习方法，并在eICU协作研究数据库上应用基于GLM与XGBoost的模型。结果表明，迁移学习在所有诊断类别中均优于仅基于特定诊断数据训练的模型，也优于单独使用公认的重症严重程度评分APACHE IVa；同时其校准性能优于合并数据训练的模型。研究还发现，对于二分类结局，Youden截断点比传统0.5更合适，且迁移学习在多种截断标准下均保持稳定高预测性能。
原文摘要 · Abstract (English)
In the intensive care unit, the underlying causes of critical illness vary substantially across diagnoses, yet prediction models accounting for diagnostic heterogeneity have not been systematically studied. To address the gap, we evaluate transfer learning approaches for diagnosis-specific mortality prediction and apply both GLM- and XGBoost-based models to the eICU Collaborative Research Database. Our results demonstrate that transfer learning consistently outperforms models trained only on diagnosis-specific data and those using a well-known ICU severity-of-illness score, i.e., APACHE IVa, alone, while also achieving better calibration than models trained on the pooled data. Our findings also suggest that the Youden cutoff is a more appropriate decision threshold than the conventional 0.5 for binary outcomes, and that transfer learning maintains consistently high predictive performance across various cutoff criteria.
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