...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multiple Sclerosis Journal ?C Experimental, Translational and Clinical >Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression
【24h】

Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression

机译：二次逐步多发性硬化的机器学习：一种改进的短期残疾进展预测模型

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background Enhanced prediction of progression in secondary progressive multiple sclerosis (SPMS) could improve clinical trial design. Machine learning (ML) algorithms are methods for training predictive models with minimal human intervention. Objective To evaluate individual and ensemble model performance built using decision tree (DT)-based algorithms compared to logistic regression (LR) and support vector machines (SVMs) for predicting SPMS disability progression. Methods SPMS participants (n?=?485) enrolled in a 2-year placebo-controlled (negative) trial assessing the efficacy of MBP8298 were classified as progressors if a 6-month sustained increase in Expanded Disability Status Scale (EDSS) (≥1.0 or ≥0.5 for a baseline of ≤5.5 or ≥6.0 respectively) was observed. Variables included EDSS, Multiple Sclerosis Functional Composite component scores, T2 lesion volume, brain parenchymal fraction, disease duration, age, and sex. Area under the receiver operating characteristic curve (AUC) was the primary outcome for model evaluation. Results Three DT-based models had greater AUCs (61.8%, 60.7%, and 60.2%) than independent and ensemble SVM (52.4% and 51.0%) and LR (49.5% and 51.1%). Conclusion SPMS disability progression was best predicted by non-parametric ML. If confirmed, ML could select those with highest progression risk for inclusion in SPMS trial cohorts and reduce the number of low-risk individuals exposed to experimental therapies.
机译：背景技术次级逐步多发性硬化症（SPM）中进展的增强预测可以改善临床试验设计。机器学习（ML）算法是培养具有最小人类干预的预测模型的方法。目的评估使用决策树（DT）的算法建造的个人和集合模型性能与逻辑回归（LR）和支持向量机（SVM）进行比较，以预测SPMS残疾进展。方法SPM参与者（n？= 485）注册了2年的安慰剂控制（负）试验评估，评估MBP8298的疗效，如果扩增残疾状态规模（EDS）的持续增加6个月持续增加，则被归类为进展者（≥1.0观察到≤5.5或≥6.0的基线≥0.5）。变量包括EDS，多发性硬化功能复合组分分数，T2病变体积，脑实质级分，疾病持续时间，年龄和性别。接收器操作特征曲线（AUC）下的区域是模型评估的主要结果。结果三种基于DT的模型比独立和合奏SVM（52.4％和51.0％）和LR（49.5％和51.1％）更大的AUC（61.8％，60.7％和60.2％）。结论SPMS残疾进展最佳通过非参数ML预测。如果确认，ML可以选择纳入SPM试验队列的最高进展风险的人，并减少暴露于实验疗法的低风险个体数量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号