预测脊柱转移手术后活动结果的机器学习模型的发展。

IF 2.3 Q2 ORTHOPEDICS
Asian Spine Journal Pub Date : 2023-12-01 Epub Date: 2023-12-05 DOI:10.31616/asj.2023.0051
Piya Chavalparit, Sirichai Wilartratsami, Borriwat Santipas, Piyalitt Ittichaiwong, Kanyakorn Veerakanjana, Panya Luksanapruksa
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引用次数: 0

摘要

研究设计:回顾性队列研究。目的:本研究旨在开发机器学习算法来预测脊柱转移手术后的活动结果。文献综述:脊柱转移手术后的活动状态目前难以预测。预测这一重要术后结果的能力的提高将有助于管理决策,并有助于确定现实的治疗目标。方法:这项回顾性研究包括2009年1月至2021年11月期间在泰国一所大学医学中心接受脊柱转移的患者。收集的数据包括术前参数和术后90天和180天的活动状况。采用人工神经网络、逻辑回归、CatBoost分类器、线性判别分析、极端梯度增强、额外树分类器、随机森林分类器、梯度增强分类器、轻梯度增强机、naïve贝叶斯、k近邻分类器、Ada增强分类器、决策树分类器等13种机器学习算法预测术后90天和180天的动态状态。采用受试者工作特征曲线下面积(AUC)和f1评分对模型性能进行评价。结果:共纳入167例患者。术后90天和180天可走动的患者分别为140例(81.9%)和137例(82.0%)。发现极端梯度增强算法最准确地预测180天的动态预后(AUC, 0.85;f1评分,0.90),决策树算法最准确地预测了90天的动态预后(AUC, 0.94;F1-score, 0.88)。结论:机器学习算法在预测脊柱转移术后的活动状态方面是有效的。根据我们的数据,极端梯度增强和决策树分别最好地预测脊柱转移手术后180天和90天的术后活动状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Machine-Learning Models to Predict Ambulation Outcomes Following Spinal Metastasis Surgery.

Study design: Retrospective cohort study.

Purpose: This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis.

Overview of literature: Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The improved ability to predict this important postoperative outcome would facilitate management decision-making and help in determining realistic treatment goals.

Methods: This retrospective study included patients who underwent spinal metastasis at a university-based medical center in Thailand between January 2009 and November 2021. Collected data included preoperative parameters and ambulatory status 90 and 180 days following surgery. Thirteen machine-learning algorithms, namely, artificial neural network, logistic regression, CatBoost classifier, linear discriminant analysis, extreme gradient boosting, extra trees classifier, random forest classifier, gradient boosting classifier, light gradient boosting machine, naïve Bayes, K-neighbor classifier, Ada boost classifier, and decision tree classifier were developed to predict ambulatory status 90 and 180 days following surgery. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1-score.

Results: In total, 167 patients were enrolled. The number of patients classified as ambulatory 90 and 180 days following surgery was 140 (81.9%) and 137 (82.0%), respectively. The extreme gradient boosting algorithm was found to most accurately predict 180-day ambulatory outcome (AUC, 0.85; F1-score, 0.90), and the decision tree algorithm most accurately predicted 90-day ambulatory outcome (AUC, 0.94; F1-score, 0.88).

Conclusions: Machine-learning algorithms were effective in predicting ambulatory status following surgery for spinal metastasis. Based on our data, the extreme gradient boosting and decision tree best predicted postoperative ambulatory status 180 and 90 days after spinal metastasis surgery, respectively.

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来源期刊
Asian Spine Journal
Asian Spine Journal ORTHOPEDICS-
CiteScore
5.10
自引率
4.30%
发文量
108
审稿时长
24 weeks
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