An interactive web application to identify early Parkinsonian non-tremor-dominant subtypes
An interactive web application to identify early Parkinsonian non-tremor-dominant subtypes
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DOI：
10.1007/s00415-023-12156-5
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发表时间：
2024-01-04
影响因子：
6
通讯作者：
Xiao，Lishun
中科院分区：
文献类型：
--
作者：
Xu，Xiaozhou;Gu，Wen;Xiao，Lishun
帕金森病（PD）患者存在震颤显性（TD）和非震颤显性（NTD）亚型的异质性。快速识别不同的运动亚型可能有助于制定个性化的治疗plannes.MethodsThe数据从帕金森病进展标志物倡议（PPMI）。在利用递归特征消除（RFE）识别预测器之后，七种经典的机器学习（ML）模型，包括逻辑回归、支持向量机、决策树、随机森林、极端梯度提升等，结果RFE产生的特征子集包含20个特征，包括部分临床评估和脑脊液α-synuclein（CSF α-syn）。在RFE子集中拟合的ML模型在测试和验证集中表现更好。性能最好的模型是具有多项式核的支持向量机（P-SVM），其AUC为0.898。5倍重复交叉验证表明，加入CSF α-syn的P-SVM模型的预测效果优于不加入CSF α-syn的P-SVM模型（P= 0.034）。Shapley加性解释图（SHAP）说明了各特征的水平如何影响NTD亚型的预测概率。结论基于RFE特征子集构建的P-SVM模型开发了一个交互式Web应用程序。它可以识别PD患者当前的运动亚型，更容易了解患者的状态并制定个性化的治疗方案。
BackgroundParkinson’s disease (PD) patients with tremor-dominant (TD) and non-tremor-dominant (NTD) subtypes exhibit heterogeneity. Rapid identification of different motor subtypes may help to develop personalized treatment plans.MethodsThe data were acquired from the Parkinson’s Disease Progression Marker Initiative (PPMI). Following the identification of predictors utilizing recursive feature elimination (RFE), seven classical machine learning (ML) models, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting, etc., were trained to predict patients’ motor subtypes, evaluating the performance of models through the area under the receiver operating characteristic curve (AUC) and validating by the follow-up data.ResultsThe feature subset engendered by RFE encompassed 20 features, comprising some clinical assessments and cerebrospinal fluid α-synuclein (CSF α-syn). ML models fitted in the RFE subset performed better in the test and validation sets. The best performing model was support vector machines with the polynomial kernel (P-SVM), achieving an AUC of 0.898. Five-fold repeated cross-validation showed the P-SVM model with CSF α-syn performed better than the model without CSF α-syn (P= 0.034). The Shapley additive explanation plot (SHAP) illustrated that how the levels of each feature affect the predicted probability as NTD subtypes.ConclusionAn interactive web application was developed based on the P-SVM model constructed from feature subset by RFE. It can identify the current motor subtypes of PD patients, making it easier to understand the status of patients and develop personalized treatment plans.