使用贝叶斯网络检测肺癌:对丹麦高风险人群的回顾性发展和验证研究
Margrethe Bang Henriksen1,2, Florian Van Daalen3, Leonard Wee3
1Department of Oncology, Vejle University Hospital, Vejle, Denmark.
Cancer medicine
|January 31, 2025
概括
贝叶斯网络 (BN) 模型表现出弹性和与机器学习 (ML) 模型用于肺癌 (LC) 检测的可比性,即使缺少高达30%的数据. 这些发现凸显了BN作为未来LC风险评估工具的可行方法.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 肺癌 (LC) 是全球癌症死亡的主要原因.
- 目前的LC查依赖于年龄和吸烟史,但需要更先进的风险模型.
- 贝叶斯网络 (BNs) 为疾病检测提供了一个概率方法.
研究的目的:
- 开发和评估用于肺癌 (LC) 检测的贝叶斯网络 (BN) 模型.
- 评估BN模型对缺少数据的弹性.
- 将BN模型的性能与传统机器学习 (ML) 模型进行比较.
主要方法:
- 分析了来自丹麦南部的9940名患者记录 (2009-2018).
- 包括变量:年龄,性别,吸烟状态和实验室结果.
- 实验涉及不同缺失数据百分比 (0-30%) 和BN配置.
主要成果:
- 贝叶斯网络模型在缺失的数据级别中保持了稳定的性能 (AUC 0.737-0.757),与ML模型 (AUC 0.77) 相比.
- BN结构和离散方法对模型有效性的影响最小.
- BNs表现出良好的校准和临床实用性,特别是在预测风险超过5%的情况下.
结论:
- 即使缺少大量数据 (高达30%),BN模型也是强大而有效的.
- BNs的性能,校准和临床实用性与ML模型用于LC检测具有可比性.
- BNs代表了开发未来肺癌风险预测模型的有希望的方法.
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