A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage.
A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage.
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DOI：
10.3389/fneur.2022.791547
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发表时间：
2022
影响因子：
3.4
通讯作者：
Chen Q
中科院分区：
文献类型：
--
作者：
Hu P;Liu Y;Li Y;Guo G;Su Z;Gao X;Chen J;Qi Y;Xu Y;Yan T;Ye L;Sun Q;Deng G;Zhang H;Chen Q
树型算法作为一种应用最广泛的机器学习方法，尚未应用于老年蛛网膜下腔出血（aSAH）患者迟发性脑缺血（DCI）的预测。因此，本研究旨在开发传统的回归和基于树的模型，并确定哪种模型对aSAH后住院老年患者的DCI发展具有更好的预测性能。这是一项多中心、回顾性、观察性队列研究，分析了年龄≥ 60岁的aSAH老年患者。我们将多中心数据随机分为模型训练和验证队列，比例为70- 30%。一个传统的回归和基于树的模型，如最小绝对收缩和选择算子（LASSO），决策树（DT），随机森林（RF）和极端梯度提升（XGBoost），被开发出来。采用准确度、灵敏度、特异度、精确度-召回率曲线下面积（AUC-PR）和受试者工作特征曲线下面积（AUC-ROC）及其95% CI评价模型预测性能。进行DeLong检验以计算模型之间的统计学差异。最后，我们计算了每个特征的重要性权重，以可视化对DCI的贡献。模型训练和验证队列中分别有111例和42例患者，53例发生了DCI。根据AUC-ROC值进行模型内部验证，DT为0.836（95% CI：0.747-0.926，p = 0.15），RF为1（95% CI：1-1，p < 0.05），XGBoost为0.931（95% CI：0.885-0.978，p = 0.01）优于LASSO为0.793（95% CI：0.692-0.893）。然而，LASSO的AUC-ROC值最高，为0.894（95% CI：0.8-0.989），而DT为0.764（95% CI：0.6-0.928，p = 0.05），RF为0.821（95% CI：0.683-0.959，p = 0.27），XGBoost为0.865（95% CI：0.751-0.979，p = 0.69）。此外，在外部验证中，LASSO的AUC-PR值最高，为0.681，而DT为0.615，RF为0.667，XGBoost为0.622。此外，我们发现蛛网膜下腔血凝块的CT值、动脉瘤治疗和白色血细胞计数是老年aSAH患者DCI的最重要特征。在外部验证中，LASSO的预测能力上级基于树的模型。因此，我们推荐传统的LASSO回归模型来预测老年aSAH患者的DCI。
As a most widely used machine learning method, tree-based algorithms have not been applied to predict delayed cerebral ischemia (DCI) in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH). Hence, this study aims to develop the conventional regression and tree-based models and determine which model has better prediction performance for DCI development in hospitalized elderly patients after aSAH. This was a multicenter, retrospective, observational cohort study analyzing elderly patients with aSAH aged 60 years and older. We randomly divided the multicentral data into model training and validation cohort in a ratio of 70–30%. One conventional regression and tree-based model, such as least absolute shrinkage and selection operator (LASSO), decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost), was developed. Accuracy, sensitivity, specificity, area under the precision-recall curve (AUC-PR), and area under the receiver operating characteristic curve (AUC-ROC) with 95% CI were employed to evaluate the model prediction performance. A DeLong test was conducted to calculate the statistical differences among models. Finally, we figured the importance weight of each feature to visualize the contribution on DCI. There were 111 and 42 patients in the model training and validation cohorts, and 53 cases developed DCI. According to AUC-ROC value in the model internal validation, DT of 0.836 (95% CI: 0.747–0.926, p = 0.15), RF of 1 (95% CI: 1–1, p < 0.05), and XGBoost of 0.931 (95% CI: 0.885–0.978, p = 0.01) outperformed LASSO of 0.793 (95% CI: 0.692–0.893). However, the LASSO scored a highest AUC-ROC value of 0.894 (95% CI: 0.8–0.989) than DT of 0.764 (95% CI: 0.6–0.928, p = 0.05), RF of 0.821 (95% CI: 0.683–0.959, p = 0.27), and XGBoost of 0.865 (95% CI: 0.751–0.979, p = 0.69) in independent external validation. Moreover, the LASSO had a highest AUC-PR value of 0.681 than DT of 0.615, RF of 0.667, and XGBoost of 0.622 in external validation. In addition, we found that CT values of subarachnoid clots, aneurysm therapy, and white blood cell counts were the most important features for DCI in elderly patients with aSAH. The LASSO had a superior prediction power than tree-based models in external validation. As a result, we recommend the conventional LASSO regression model to predict DCI in elderly patients with aSAH.
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影响因子：
5.5
作者：
Fang, Yuan-Jian;Mei, Shu-Hao;Chen, Sheng
通讯作者：
Chen, Sheng
影响因子：
11
作者：
de Rooij, N. K.;Linn, F. H. H.;Rinkel, G. J. E.
通讯作者：
Rinkel, G. J. E.
DOI：
10.1109/titb.2009.2020434
发表时间：
2009-09-01
影响因子：
--
作者：
de Toledo, Paula;Rios, Pablo M.;Lagares, Alfonso
通讯作者：
Lagares, Alfonso
影响因子：
5.7
作者：
Duan, Wanying;Pan, Yuesong;Liu, Liping
通讯作者：
Liu, Liping
DOI：
10.1056/nejmp1702071
发表时间：
2017-06-29
期刊：
The New England journal of medicine
影响因子：
--
作者：
Chen JH;Asch SM
通讯作者：
Asch SM