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整合生殖和临床变量来预测多发性硬化症产后残疾结果,使用机器学习

Murat Emec¹, Said Alizada², Yasemin Simsek³

¹Istanbul Nisantasi University, Department of Aviation Electrical and Electronics, Sarıyer, Istanbul, 34398, Turkey.

Multiple sclerosis and related disorders

|March 10, 2026

概括

机器学习可以准确地预测患有多发性硬化症 (MS) 的女性产后残疾变化. 产后复发是主要因素,但生殖史和产科细节也提供了预后见解.

科学领域:

神经学神经学
免疫学免疫学免疫学
数据科学数据科学数据科学

背景情况:

怀孕会改变患有多发性硬化症 (MS) 的女性的免疫系统.
产后疾病的重新激活是MS患者的一个重要问题.
怀孕对MS残疾进展的长期影响需要进一步调查.

研究的目的:

预测患有MS的女性产后残疾变化 (基于EDSS).
使用机器学习模型,结合临床和人口统计学怀孕变量.
确定MS中产后残疾进展的关键预测因素.

主要方法:

对662名妇女和909个怀孕的回顾性研究.
工程特征:EDSS分数,疾病持续时间,年龄,产后复发,产科因素.
采用随机森林,XGBoost,弹性网和支持向量分类器模型与交叉验证.

主要成果:

分类模型显示了高精度 (85-88%) 和F1分数 (0.84-0.87).
产后复发,疾病持续时间和母亲年龄是主要预测因素.
多重怀孕和产科因素 (分娩类型,母乳养) 提供了额外的预后价值.

结论:

机器学习模型有效预测MS产后残疾变化.
产后复发是主要因素,生殖和产科变量提供了补充的见解.
产后期对于MS管理至关重要,需要个性化的预后框架.

关键词:

失能进展失能进展机器学习机器学习在分娩后复发.预测建模预测建模怀孕怀孕怀孕怀孕生殖变量生殖变量

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