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塞鲁Net-MS:用于多发性硬化症风险预测的双阶段可解释框架,基于SHAP的可解释性

Serra Aksoy¹, Pinar Demircioglu², Ismail Bogrekci²

¹Institute of Computer Science, Ludwig Maximilian University of Munich (LMU), Oettingenstrasse 67, 80538 Munich, Germany.

Neurology international

|September 26, 2025

概括

一个新的两阶段机器学习模型准确地预测了多发性硬化症 (MS) 从临床隔离综合征 (CIS) 的转化. 可解释AI (SHAP) 提供透明的,患者特异性的风险因素,增强MS诊断的临床信任和采用.

科学领域:

神经学神经学
人工智能的人工智能
生物统计学生物统计学

背景情况:

多发性硬化症 (MS) 诊断需要早期识别临床隔离综合征 (CIS) 以临床确定MS.
目前用于MS预测的机器学习 (ML) 模型缺乏可解释性,阻碍了临床信任.
存在对可解释AI (XAI) 的需求,以解决人口偏见并改善多发性硬化风险分层.

研究的目的:

开发和验证一个新的,可解释的两阶段ML框架 (SeruNet-MS),用于预测CIS转换为MS.
在MS风险预测中减轻人口偏见.
通过透明的,针对患者的风险因素解释,提高临床采用率.

主要方法:

通过使用两阶段的ML框架 (SeruNet-MS) 分析了177名CIS患者.
第一个阶段:对人口特征进行后勤回归. 第二阶段:纳入了25个临床/症状特征 (MRI,脑脊髓细胞生物标志物等). ) 的情况.
使用SHAP (夏普利添加式扩展) 进行患者层面的解释性和风险因素归因.

主要成果:

两阶段模型实现了高性能:ROC-AUC 0.909,准确度 0.806,精度 0.842,召回 0.800.
交叉验证证实了稳定的性能 (AUC 0.838 ± 0.095).
在SHAP分析中,确定了周周结膜病变,橄克隆带和症状复杂性作为关键预测因素.

结论:

两个阶段的方法有效地将人口偏见与临床风险因素分开.
SHAP解释为临床医生提供可操作的,个性化的洞察力,用于MS风险评估.
这种可解释的框架促进了MS临床实践中AI的采用.

关键词:

在SHAP分析中,我们分析了SHAP.这就是SeruNet-MS.临床决策支持临床决策支持临床隔离综合征 (CIS) 是一种临床隔离综合征.人口统计偏见人口统计偏见疾病的进展疾病的进展可以解释的人工智能AI 多发性硬化症 (MS) 是一种疾病.神经生物标志物神经生物标志物预测建模预测建模机器学习的两个阶段.

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