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使用可解释的机器学习算法预测患者中风的严重程度.

Amir Sorayaie Azar1,2, Tahereh Samimi3,4, Ghanbar Tavassoli3,4,5

  • 1SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.

European journal of medical research
|November 14, 2024
PubMed
概括
此摘要是机器生成的。

机器学习模型使用快速动脉阻塞评估 (RACE) 和国家卫生研究院中风量表 (NIHSS) 准确预测中风严重程度. 随机森林实现了最高的准确性,识别了关键预测因素,如甘油三水平和年龄.

关键词:
可以解释的机器学习机器学习 机器学习预测 预测 预测脑卒中的严重程度 脑卒中严重程度

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科学领域:

  • 神经学 神经学
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 在全球范围内,中风是导致死亡的主要原因,因此需要对患者结果进行准确的严重性评估.
  • 目前的中风评估工具包括快速动脉阻塞评估 (RACE) 和国家卫生研究院中风量表 (NIHSS).
  • 预测中风的严重程度对于医疗保健系统的有效治疗和资源配置至关重要.

研究的目的:

  • 应用机器学习 (ML) 算法来预测中风的严重程度.
  • 使用RACE和NIHSS尺度对ML模型的性能进行比较.
  • 确定影响中风严重程度预测的关键临床特征.

主要方法:

  • 利用来自伊朗乌尔米亚的两个数据集进行RACE和NIHSS中风严重程度评估.
  • 应用了七个ML算法:KNN,DT,RF,AdaBoost,XGBoost,SVM和ANN. 这些算法包括:KNN,DT,RF,AdaBoost,XGBoost,SVM和ANN.
  • 采用超参数调整的网格搜索和特征解释性的SHapley添加式解释 (SHAP).

主要成果:

  • 随机森林 (RF) 模型表现出卓越的性能,在RACE中达到92.68%的精度,在NIHSS中达到91.19%的精度.
  • 曲线下的面积 (AUC) 值达到RACE的92.02%和NIHSS的97.86%.
  • SHAP分析强调了甘油三水平,住院时间和年龄作为中风严重程度的重要预测因素.

结论:

  • 这项研究率先将ML应用于RACE和NIHSS尺度,用于预测中风严重程度.
  • SHAP分析提高了模型的解释性,促进了对ML工具的临床信任.
  • 开发的ML模型为临床医生预测中风严重程度提供了宝贵的帮助.