使用机器学习技术来预测肺癌的生存率
Rodrigo Henrich1,2, Rafael H Bordini1, Isabel H Manssour1
1Pontifical Catholic University of Rio Grande do Sul, PUCRS.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究开发了一种人工智能模型来预测肺癌患者的生存时间,在分类长期或短期生存时达到84.6%的准确性. 该模型增强了针对个性化肺癌治疗的临床决策.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 肺癌仍然是全球癌症相关死亡的主要原因.
- 及时诊断和量身定制的治疗对于改善患者的结果至关重要.
- 人工智能 (AI) 具有提高瘤学临床决策支持的潜力.
研究的目的:
- 开发和评估用于预测肺癌患者生存率的机器学习模型.
- 将患者的生存时间分为"长"或"短"的时间框架.
- 使用SHAP值来提高预测模型的可解释性.
主要方法:
- 将机器学习技术应用于肺癌患者的数据集.
- 开发一种用于生存时间分类的预测模型.
- 使用SHAP (夏普利添加式扩展) 进行模型解释.
主要成果:
- 开发的机器学习模型在预测生存分类方面实现了84.6%的准确性.
- SHAP分析确定了影响生存时间预测的关键变量.
- 该模型显示了支持肺癌护理临床决策的潜力.
结论:
- 机器学习模型可以有效地预测肺癌患者的生存率.
- 可解释的人工智能 (SHAP) 提高了对临床环境中的预测模型的信任和理解.
- 这种方法可以帮助个性化治疗策略和改善肺癌患者管理.
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