可解释的人工智能与电子健康记录集成在瘤学中的应用
1West China Hospital, Sichuan University, Chengdu 610000, Sichuan, China.
Exploration of targeted anti-tumor therapy
|February 9, 2026
概括
可解释的人工智能 (XAI) 可以通过使用电子健康记录 (EHR) 提高对瘤护理的信任和安全. 然而,对于广泛的临床采用,必须解决评估,可复制性和公平性方面的挑战.
科学领域:
- 人工智能在医学中的应用
- 瘤学 数据科学 数据科学
- 临床信息学 临床信息学
背景情况:
- 机器学习 (ML) 和深度学习 (DL) 显示出通过电子健康记录 (EHR) 改善瘤护理的希望.
- 模型的不透明性阻碍了临床医生的信任,监管机构的批准和这些强大的工具的安全部署.
- 可解释的人工智能 (XAI) 方法对于使ML/DL模型在临床环境中变得可理解和可操作至关重要.
研究的目的:
- 总结目前XAI对基于EHR的瘤学任务的方法.
- 确定XAI在瘤学的评估,可重现性,临床实用性和公平性的关键挑战.
- 提出建议和研究方向,以加快XAI在瘤学中的安全采用.
主要方法:
- 对EHR数据应用的常见XAI类别的审查,包括特征重要性,可解释模型,注意力机制和规则提取.
- 对常见方法的分析,如使用SHapley添加式扩展 (SHAP) 的基于树的模型和通用添加式模型.
- 确定关于报告标准,临床效用评估,可重复性,外部验证和公平性的研究缺口.
主要成果:
- 在最近的EHR研究中,以SHAP解释为基础的树模型占主导地位.
- 当透明度至关重要时,使用内在可解释的模型 (例如,通用添加模型,规则集).
- 在一致的报告,正式的临床效用评估,可重复性,外部验证和公平性考虑方面存在重大差距.
结论:
- 将XAI与EHR驱动的瘤模型集成是有希望的,但需要进一步开发.
- 应对评估框架,可重复的管道,验证和公平意识的设计方面的挑战至关重要.
- 优先考虑临床上有意义的解释,并研究它们对决策和患者结果的影响,对于推进该领域至关重要.
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