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使用有氧能力数据用于多发性硬化症EDSS得分估计:一种机器学习方法.

Seda Arslan Tuncer¹, Cagla Danacı^1,2, Furkan Bilek³

¹Software Engineering, Faculty of Engineering, Firat University, 23119 Elazığ, Turkey.

Diagnostics (Basel, Switzerland)

|June 27, 2024

概括

机器学习使用有氧能力数据准确预测多发性硬化症 (MS) 的进展. 这种方法提高了扩展残疾状况量表 (EDSS) 评分的可靠性,减少了潜在的医生变化,并改善了患者护理.

关键词:

扩展的残疾状况规模.有氧能力有氧能力.梯度增强可以提高梯度.机器学习是机器学习.多发性硬化症多发性硬化症

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科学领域:

神经学神经学
生物医学工程生物医学工程
数据科学数据科学数据科学

背景情况:

扩展残疾状态量表 (EDSS) 对于监测多发性硬化症 (MS) 进展和治疗疗效至关重要.
医生之间不一致的EDSS评分对可靠的患者评估构成了挑战.
开发自主解决方案是必要的,以提高EDSS评估的客观性和可靠性.

研究的目的:

提出一种机器学习 (ML) 方法来预测MS患者的EDSS分数 (PwMS).
利用有氧能力和心血管数据作为ML模型的输入特征.
提高EDSS评估的可靠性,并减轻得分差异导致的并发症.

主要方法:

从PwMS收集心血管和有氧能力数据,包括通风,心率和氧气消耗,从PwMS.
使用的机器学习算法:CatBoost,梯度提升机 (GBM),极端梯度提升 (XGBoost) 和决策树 (DT).
使用诸如平均绝对误差 (MAE),根平均平方误差 (RMSE) 和R平方等指标评估模型性能.

主要成果:

XGBoost算法在预测EDSS分数方面表现出最高的准确性.
实现的绩效指标:MAE为0.26,RMSE为0.4,R-平方为0.68.
基于生理数据,ML方法有效预测EDSS得分.

结论:

机器学习,特别是XGBoost,提供了一种可靠的方法来预测PwMS中的EDSS分数.
有氧能力和心血管参数是MS残疾的重要预测因素.
这种基于ML的方法可以提高EDSS评估的一致性和客观性.