XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.
XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.
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XGBoost 是一种机器学习方法，可预测颈脊髓损伤患者的神经功能恢复。
DOI：
10.1089/neur.2020.0009
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发表时间：
2020
影响因子：
2.4
通讯作者：
Tominaga T
中科院分区：
文献类型：
--
作者：
Inoue T;Ichikawa D;Ueno T;Cheong M;Inoue T;Whetstone WD;Endo T;Nizuma K;Tominaga T
由于患者特征、治疗策略和影像学结果的异质性，准确预测颈脊髓损伤 (SCI) 患者的神经系统结果很困难。尽管机器学习算法可能会提高各个领域结果预测的准确性，但关于其在 SCI 管理中的功效的信息有限。我们分析了 165 名颈椎 SCI 患者的数据，并提取了预测预后的重要因素。极限梯度提升 (XGBoost) 作为一种机器学习模型，用于评估机器学习算法与传统方法（例如逻辑回归或决策树）相比预测神经系统结果的可靠性。我们使用定期获得的数据作为预测因子，例如人口统计数据、磁共振变量和治疗策略。应用包括 XGBoost、逻辑回归和决策树在内的预测工具来预测受伤后 6 个月功能性运动状态（ASIA [美国脊柱损伤协会] 损伤量表 [AIS] D 和 E）的神经学改善。我们评估了预测性能，包括准确性和受试者工作特征曲线下面积 (AUC)。对于颈椎 SCI 患者神经功能改善的预测，XGBoost 的准确率最高（81.1%），其次是逻辑回归（80.6%）和决策树（78.8%）。关于 AUC，逻辑回归显示为 0.877，其次是 XGBoost (0.867) 和决策树 (0.753)。 XGBoost 可靠地预测了颈椎 SCI 患者的神经系统改变。预测机器学习算法的利用可以通过患者的治疗前分类来增强个性化的管理选择。
The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.
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影响因子：
7.2
作者：
DEYO, RA;CHERKIN, DC;CIOL, MA
通讯作者：
CIOL, MA
影响因子：
2.3
作者：
Magu S;Singh D;Yadav RK;Bala M
通讯作者：
Bala M
影响因子：
3
作者：
BONDURANT, FJ;COTLER, HB;HARRIS, JH
通讯作者：
HARRIS, JH
影响因子：
2.2
作者：
Alexander, M. S.;Anderson, K. D.;Biering-Sorensen, F.;Blight, A. R.;Brannon, R.;Bryce, T. N.;Creasey, G.;Catz, A.;Curt, A.;Donovan, W.;Ditunno, J.;Ellaway, P.;Finnerup, N. B.;Graves, D. E.;Haynes, B. A.;Heinemann, A. W.;Jackson, A. B.;Johnston, M. V.;Kalpakjian, C. Z.;Kleitman, N.;Krassioukov, A.;Krogh, K.;Lammertse, D.;Magasi, S.;Mulcahey, M. J.;Schurch, B.;Sherwood, A.;Steeves, J. D.;Stiens, S.;Tulsky, D. S.;van Hedel, H. J. A.;Whiteneck, G.
通讯作者：
Whiteneck, G.
使用决策树模型进行交叉口交通预测
影响因子：
2.7
作者：
Alajali, Walaa;Zhou, Wei;Wang, Yu
通讯作者：
Wang, Yu