针对心脏出院分型的不平衡数据，提出加权提升框架以提升高风险类型识别。
CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping
- 基于类别权重与缺失值指示增强，动态调整样本重要性。
- 在五类心脏分型中，优先类别F1达最优，整体准确率超越树模型、集成与神经网络基线。
- 适合临床部署，兼顾可解释性与对高危病人的敏感识别。
心脏出院分型有助于指导出院后治疗与随访，但真实医疗记录常存在不完整和类别不平衡问题，增加遗漏高风险分型的风险。本文提出CW-B，一种面向真实世界类别不平衡与缺失数据的五分类心脏出院分型类加权提升XGBoost流程。CW-B结合折内类别平衡采样权重、缺失值指示符增强及类别级错误审计机制，在保持决策可解释与可审计的前提下，提升对临床优先关注分型的识别能力。在五折分层交叉验证中，CW-B在准确率、宏平均F1、平衡准确率及优先类别F1上均优于树模型、集成方法与神经网络基线。结果表明，CW-B为真实临床场景下更可靠的多类别心脏分型提供了实用且可部署的解决方案。
原文摘要 · Abstract (English)
Cardiac discharge phenotyping informs post-discharge treatment and follow-up, but real-world records are often incomplete and class-imbalanced, increasing the risk of missed high-risk phenotypes. We propose CW-B, a clinical risk-aligned class-weighted XGBoost pipeline for five-class cardiac discharge phenotyping under real-world class imbalance and missingness. CW-B combines fold-specific class-balanced instance weighting, missingness-indicator augmentation, and classwise error auditing to improve recognition of clinically prioritized phenotypes while preserving interpretable and auditable decision logic. In five-fold stratified cross-validation, CW-B achieves the best Accuracy, Macro-F1, Balanced Accuracy, and Prioritized F1 among tree-based, ensemble, and neural baselines. Overall, CW-B provides a practical and deployment-oriented approach for more reliable cardiac discharge phenotyping in real-world clinical settings.
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