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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.

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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.

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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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.