Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy

ZHANG Zhan; JIANG Delong; WANG Qingyan; WANG Pengqin

doi:10.13703/j.0255-2930.20241028-0001

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Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy

Acupuncture and Intelligent Medicine | 更新时间：2025-05-15

- Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy
- Chinese Acupuncture & Moxibustion Vol. 45, Issue 5, Pages: 559-567(2025)
- 作者机构：
  
  辽宁中医药大学中医学院，沈阳　110847
  辽宁中医药大学附属医院脑病康复二科，沈阳　110032
- 作者简介：
- 基金信息：
- DOI：10.13703/j.0255-2930.20241028-0001
  CLC：
- Received：28 October 2024，
  
  Online First：20 February 2025，
  
  Published：12 May 2025
- 稿件说明：
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ZHANG Zhan, JIANG Delong, WANG Qingyan, et al. Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy[J]. Chinese Acupuncture & Moxibustion, 2025, 45(5): 559-567.
DOI：

ZHANG Zhan, JIANG Delong, WANG Qingyan, et al. Construction of an interpretable machine learning-based prediction model for the clinical effect on ischemic stroke in treatment with eye acupuncture combined with rehabilitation therapy[J]. Chinese Acupuncture & Moxibustion, 2025, 45(5): 559-567. DOI： 10.13703/j.0255-2930.20241028-0001.

摘要

目的：

基于可解释机器学习构建眼针带针康复疗法治疗缺血性脑卒中的临床疗效预测模型。

方法：

收集2020年1月1日至2024年10月1日于辽宁中医药大学附属医院脑病康复二科病房接受眼针带针康复治疗的470例缺血性脑卒中患者临床资料。运用治疗前后改良Barthel指数（MBI）评分将患者分为有效组（291例）和无效组（179例）。采用随机森林与递归特征消除结合交叉验证筛选影响患者临床疗效的预测因子，根据筛选结果建立7种具有不同原理的代表性机器学习模型，通过接受者操作特征（ROC）曲线、校准曲线和临床决策曲线（DCA）评价最佳模型的预测效果，最后采用沙普利加性解释（SHAP）框架解释最佳模型的预测结果。

结果：

①所有机器学习模型的曲线下面积均在85%以上，其中随机森林模型表现出最佳预测能力，曲线下面积达到0.96，精确率为0.87；②校准曲线的预测概率与实际概率体现较好的预测一致性；③DCA曲线在0.1~1.0范围内净获益率高于风险阈值，提示模型效果较好；④SHAP解释了影响模型预测效果的变量特征值，即接受治疗天数多、治疗前MBI评分低、纤维蛋白原（FBG）水平低、发病天数少和年龄低可提升眼针带针康复疗法的临床疗效。

结论：

本研究构建的临床疗效预测模型表现出良好性能，有助于辅助医生制定个性化治疗方案，并区分出眼针带针康复疗法的获益人群及帮助寻找临床疗效优势人群，为眼针带针康复疗法治疗缺血性脑卒中提供更多思路。

Abstract

Objective

To construct a prediction model for the clinical effect of eye acupuncture combined with rehabilitation therapy on ischemic stroke based on interpretable machine learning.

Methods

From January 1st

2020 to October 1st

2024

the clinical data of 470 patients with ischemic stroke were collected in the the Second Department of Encephalopathy Rehabilitation of the Affiliated Hospital of Liaoning University of TCM. The modified Barthel index (MBI) score before and after treatment was used to divide the patients into an effect group (291 cases) and a non-effect group (179 cases). Random forest and recursive feature elimination with cross-validation were combined to screen the predictors of the therapeutic effect of patients. Seven representative machine learning models with different principles were established according to the screening results. The predictive effect of the best model was evaluated by receiver operating characteristics (ROC)

calibration

and clinical decision-making (DCA) curves. Finally

the Shapley additive explanation (SHAP) framework was used to interpret the prediction results of the best model.

Result

①All the machine learning models presented the area under curve (AUC) to be above 85%. Of these models

the random forest model showed the best prediction ability

with AUC of 0.96 and the precision of 0.87. ②The prediction probability of calibration curve and the actual probability showed a good prediction consistency. ③The net benefit rate of DCA curve in the range of 0.1 to 1.0 was higher than the risk threshold

indicating a good effect of model. ④SHAP explained the characteristic values of variables that affected the prediction effect of the model

meaning

more days of treatment

lower MBI score before treatment

lower level of fibrinogen

shorter days of onset and younger age. These values demonstrated the better effect of eye acupuncture rehabilitation therapy.

Conclusion

The rehabilitation effect prediction model constructed in this study presents a good performance

which is conductive to assisting doctors in formulating targeted personalized rehabilitation programs

and identifying the benefit groups of eye acupuncture combined with rehabilitation therapy and finding the advantageous groups with clinical effect. It provides more ideas for the treatment of ischemic stroke with eye acupuncture combined with rehabilitation therapy.

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