患者报告的结果作为多发性硬化症中残疾演变的预测因素:可解释的机器学习方法
Federica Di Antonio1, Giampaolo Brichetto2, Andrea Tacchino3
1NeuroBRITE Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy; Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy.
Multiple sclerosis (Houndmills, Basingstoke, England)
|January 30, 2026
概括
机器学习使用常规数据准确预测多发性硬化症残疾进展. 这种可解释的模型有助于临床医生个性化患者护理和干预,以获得更好的结果.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 临床研究 临床研究
背景情况:
- 多发性硬化症 (MS) 的残疾进展是不可预测的,阻碍了个性化患者护理.
- 机器学习 (ML) 提供了整合患者报告的结果 (PRO) 和临床医生评估的结果 (CAO) 预测MS轨迹的潜力.
- 开发预测模型对于定制MS管理干预措施至关重要.
研究的目的:
- 开发和验证可解释的ML模型,用于预测MS患者的残疾累积轨迹.
- 评估模型在预测残疾随时间变化的表现.
- 确定MS中残疾进展的关键预测因素.
主要方法:
- 使用了1176名多发性硬化症患者的多中心数据集,随访时间长达8年.
- 使用基线临床变量,PRO和初始风险类来训练一个随机森林模型,以预测2,3,4和5年后的残疾累积.
- 用准确度,曲线下的面积和生存分析来评估模型性能.
主要成果:
- 最终的队列包括437名患者.
- 在预测残疾变化方面,ML模型在2年后达到0.82的准确度,在5年后达到0.73的准确度.
- 最初的风险类别和基线扩展残疾状况尺度 (EDSS) 是残疾累积的最重要的预测因素.
结论:
- 开发的ML模型为MS残疾进展提供了可靠和可解释的预测.
- 这种工具可以通过利用常规临床数据来支持临床决策.
- 该模型捕捉时间依赖模式的能力有助于理解疾病轨迹.
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