基于可解释机器学习的临界癌症患者 Delirium 的死亡率预测:一个回顾性队列研究
Yang He1, Ning Liu1, Sicheng Hao2
1Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Asia-Pacific journal of oncology nursing
|August 1, 2025
概括
机器学习准确地预测了妄的癌症患者的28天死亡率. CatBoost模型确定了关键的风险因素,有助于对这个脆弱群体进行早期干预.
科学领域:
- 在瘤学瘤学.
- 临界护理医学 临界护理医学
- 数据科学数据科学数据科学
背景情况:
- 癌症患者的痴呆症复杂化了护理,增加了死亡率和医疗费用.
- 早期预测死亡率对于对这种高风险人群及时干预至关重要.
研究的目的:
- 开发一种可解释和可概括的机器学习 (ML) 模型,用于早期预测妄的癌症患者28天死亡率.
- 确定这一患者队列中死亡率的关键预测因素.
主要方法:
- 使用MIMIC-IV数据库进行回顾性队列研究.
- 开发和评估五个ML模型,包括类别提升 (CatBoost).
- 对1893年接受ICU治疗的癌症病人进行分析.
主要成果:
- CatBoost算法表现出具有最高曲线下面积 (AUC) 的优异性能.
- 28天死亡率的关键预测因素包括高格拉斯哥昏迷量表和APACHE II分数,抗生素,普罗波和血管压缩剂.
- 该模型显示出强大的表现,并提供了对死亡风险因素的可解释的见解.
结论:
- 开发了一种最佳且可解释的ML模型 (CatBoost),用于预测妄的癌症患者的28天死亡率.
- 这些发现支持早期临床决策和有针对性的干预措施.
- 这项研究强调了ML在管理患有妄想症的高风险癌症患者中的潜力.
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