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动态建模ALSFRS-R:利用神经网络利用基于人口的得分

Robert McFarlane¹, Robert Ross², Éanna Mac Domhnaill¹

¹Academic Unit of Neurology, Trinity Biomedical Sciences Institute, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin, Ireland.

|November 16, 2025

概括

一个完全连接的神经网络 (FCNN) 准确地预测了个体的肌缩侧面硬化功能评分尺度修订 (ALSFRS-R) 评分. 这个工具有助于临床实践和试验,通过从稀疏的数据预测患者的轨迹.

科学领域:

神经科学是一个神经科学.
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:

肌缩性侧面硬化症 (ALS) 呈现异质的患者轨迹,使临床管理和研究复杂化.
由于数据间距不规则和个体变异性,ALS功能评级尺度修订 (ALSFRS-R) 很难建模.
开发准确的预测工具对于基准临床试验和告知患者护理至关重要.

研究的目的:

开发和验证使用完全连接的神经网络 (FCNN) 的短期预测工具.
预测ALS (plwALS) 患者的个人ALSFRS-R轨迹.
为ALS临床试验和实践建立一个自然史基准.

主要方法:

在 PRECISION-ALS 数据集中,对来自 5,319 个 plwALS 的 29,992 个 ALSFRS-R 测量进行了回顾性分析.
在TensorFlow中开发一个三层FCNN,从历史数据中预测未来的ALSFRS-R分数.
在 PRECISION-ALS 测试套件上进行验证,并使用 PROACT 数据库进行外部验证,以线性推断为基线.

主要成果:

在PRECISION-ALS测试套件上,FCNN实现了0.0552的平均绝对误差 (MAE) (在48点ALSFRS-R尺度上为2.65分).
该模型表现出良好的通用性,在PROACT数据集上改善了MAE的0.0485 .
各种临床子组的表现一致,FCNN的表现优于线性抽象.

结论:

一个短期的FCNN有效地从稀疏,不规则的数据提供可解释的,个性化的ALSFRS-R预测.
这种方法可以帮助识别偏离预测轨迹的患者,优化咨询和监测.
FCNN模型增强了ALS患者数据的利用,以改善临床环境和研究中的决策.

关键词:

这就是ALS.在ALSFRS-R中.肌缩性侧面硬化症 (AMLS) 是一种疾病.机器学习机器学习神经网络的神经网络的神经网络

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