使用现实世界的数据用于机器学习算法来预测高级黑色素瘤的治疗反应:针对个性化癌症护理的试点研究
Richard M Brohet1, Elianne C S de Boer2, Joram M Mossink1
1Division Data Science, Department of Innovation and Science, Isala, Zwolle, the Netherlands.
JCO clinical cancer informatics
|April 4, 2025
概括
使用真实世界数据 (RWD) 的机器学习模型准确地预测了以向治疗和免疫治疗治疗的晚期黑色素瘤患者的2年生存率. 可解释的人工智能增强了对个性化治疗决策的信任和临床效用.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 现实世界数据 (RWD) 对瘤学临床决策和个性化治疗至关重要.
- 晚期黑色素瘤患者对向治疗和免疫治疗的反应有变化,需要个性化的方法.
研究的目的:
- 使用机器学习 (ML) 和RWD预测晚期黑色素瘤的临床结果.
- 应用可解释的人工智能 (XAI) 来理解个体治疗预测.
主要方法:
- 从239名黑色素瘤患者中使用RWD开发和验证了四种ML模型.
- 嵌入了用于预测建模的ML和用于模型解释性的XAI.
- 在预测2年生存期时评估模型性能.
主要成果:
- 在ML模型中,在预测2年生存率方面,AUC>80%和准确度>74%的AUC.
- 随机森林模型表现出最高的性能,AUC为0.85.
- XAI提供了对个体预测的见解,增强了模型信任和临床相关性.
结论:
- 集成的RWD和ML用于预测黑色素瘤患者的结果,展示了概念验证.
- 在临床环境中,XAI提高了ML模型的可用性和可靠性.
- 未来使用先进人工智能的研究可以进一步改善黑色素瘤的预后和预测模型.
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