用临床常规实验室指标预测非小细胞肺癌的新型预测模型:一种机器学习方法
Yuli Wang1, Na Mei1, Ziyi Zhou1
1Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, China.
BMC medical informatics and decision making
|November 18, 2024
概括
机器学习模型使用临床数据准确预测非小细胞肺癌 (NSCLC) 患者的生存率. 一个决策树模型有助于快速临床预后评估,以改善患者护理.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 医疗信息学 医疗信息学
背景情况:
- 由于有限的早期诊断和预后工具,肺癌存在显著的发病率和死亡率.
- 开发有效的预测模型对于改善非小细胞肺癌 (NSCLC) 患者结果至关重要.
研究的目的:
- 使用机器学习算法为NSCLC患者构建临床预测模型.
- 确定用于预测NSCLC生存和复发的关键实验室指数.
主要方法:
- 在初次访问时从NSCLC患者那里收集和分析了实验室指数.
- 采用十个机器学习算法来开发生存和复发的预测模型.
- 利用随机生存森林和可解释的算法来选关键变量并构建决策树.
主要成果:
- 在生存和死亡组之间观察到瘤标志物,炎症,新陈代谢和免疫指数的显著差异.
- 一个神经网络模型实现了高预测性能 (精度为0.993,AUC为0.994) 对于NSCLC患者的生存率.
- 鉴定出interleukin-6 (IL-6),可溶性interleukin-2受体 (sIL-2R) 和CEA作为关键预测因子,形成临床决策树的基础.
结论:
- 神经网络模型对NSCLC患者的生存预测能力非常出色.
- 基于IL-6,sIL-2R和CEA的决策树模型有助于快速的临床预后评估和临床决策.
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