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使用机器学习模型预测ICU淋巴瘤患者的住院死亡率

Ling Xu¹, Guang Tu², Zhonglan Cai²

¹Breast Department, Dongguan Hospital, Guangzhou University of Traditional Chinese Medicine, Dongguan, China.

|August 20, 2025

概括

机器学习模型准确地预测了入院的淋巴瘤患者的死亡率. CatBoost分类器表现最好,识别了血液尿素 (BUN) 等关键风险因素,以改善患者分层.

科学领域:

计算生物学
医疗信息学
癌症学

背景情况:

淋巴瘤是一种严重的疾病,死亡率很高,通常需要进入重症监护室 (ICU).
现有的风险分层工具 (SOFA,APACHE) 在捕捉淋巴瘤患者的复杂临床相互作用方面存在局限性.
机器学习 (ML) 通过分析广泛的临床数据集,为患者的结果提供了先进的预测能力.

研究的目的:

开发和验证用于预测ICU淋巴瘤患者住院死亡率的ML模型.
加强高风险人群的风险分层和临床决策.
利用密集护理医疗信息中心IV (MIMIC-IV) 数据库进行模型开发.

主要方法:

使用MIMIC-IV数据库的回顾性队列研究.
拉索回归用于显著风险因素的识别 (例如,BUN,血小板,PT).
开发和比较15个ML模型 (包括CatBoost分类器,后勤回归,随机森林,梯度增强,神经网络) 使用ROC/AUC分析和SHAP值.

主要成果:

包括1591名淋巴瘤患者;342人 (21. 5%) 在医院死亡.
CatBoost 分类器获得了最高的预测性能 (AUC = 0. 7766).
确定了主要的死亡预测因素:BUN,血小板和PT;SHAP分析提供了个性化的风险见解.

结论:

ML模型,特别是CatBoost分类器,有效预测ICU淋巴瘤患者的住院死亡率.
这些模型在准确性上超越了传统方法,并提供了关键的风险分层见解.
建议对外验证和临床实施,以改善这一患者群体的治疗结果.

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