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从可解释的人工智能在食道癌症团队决策中的见解

Navamayooran Thavanesan¹, Arya Farahi², Charlotte Parfitt³

¹School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK.

Computers in biology and medicine

|August 6, 2024

概括

可解释的AI揭示了年龄对食道癌症治疗决策产生重大影响,特别是在75岁以上的患者中. 这种分析有助于理解瘤学的临床决策动态.

科学领域:

在瘤学瘤学.
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

临床医生领导的质量控制对于优化瘤学决策至关重要.
可解释的人工智能 (XAI) 提供了对临床变量影响的数据驱动洞察力.
应用了XAI技术来了解食道癌治疗决策的驱动因素.

研究的目的:

检查关键临床决策驱动因素对食道癌治疗方式的影响.
利用机器学习和XAI来建模多学科团队 (MDT) 决策动态.
确定影响食道癌患者治疗途径选择的因素.

主要方法:

893名食道癌患者 (2010-2022) 的回顾性分析.
随机森林 (RF) 分类器预测了四种治疗途径:NACT+S,NACRT+S,单独手术,息.
变量重要性和部分依赖 (PD) 分析评估了临床变量影响.

主要成果:

年龄是治疗预测的一个非常重要的变量 (16.1%),与TNM分期一起.
在75岁以上的患者中,预测的治疗概率发生了显著变化.
患者的绩效状态创建了与年龄结合的影响治疗结果的集群.

结论:

XAI有效地描述了临床因素和食道癌症治疗决策之间的关系.
晚年显著影响治疗决策,特别是具有特定瘤特征的晚年.
这种方法有助于探索人工智能驱动支持的团队决策中的偏见和不一致性.

关键词:

决策方式决策方式机器学习是机器学习.多学科的团队.oesophageal 癌症消化道癌症是一种

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