Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.

IF 2.3 3区 医学 Q2 ANESTHESIOLOGY
Parisa Sezari, Zeinab Kohzadi, Ali Dabbagh, Alireza Jafari, Saba Khoshtinatan, Kamran Mottaghi, Zahra Kohzadi, Shahabedin Rahmatizadeh
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引用次数: 0

Abstract

Background: To protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequently surpassing more established techniques. This study aims to utilize machine learning techniques on predictive parameters for challenging airway management.

Methods: This study was cross-sectional. The Shahid Beheshti University of Medical Sciences in Iran's Loghman Hakim and Shahid Labbafinezhad hospitals provided 622 records in total for analysis. Using the forest of trees approach and feature importance, important features were chosen. The Synthetic Minority Oversampling Technique (SMOTE) and repeated edited nearest neighbor under-sampling were used to balance the data. Using Python and 10-fold cross-validation, seven machine learning algorithms were assessed: Logistic Regression, Support Vector Machines (SVM), Random Forest (INFORMATION-GAIN and GINI-INDEX), Decision Tree, and K-Nearest Neighbors (KNN). Metrics like F-measure, AUC, Recall, Accuracy, Specificity, and Precision were used to evaluate the performance of the model.

Results: Twenty-four important features were chosen from the original 32 features. The under-sampling strategy produced better results than SMOTE. Among the algorithms, KNN (Euclidean, Minkowski) had better performance than other algorithms. The highest values ​​for accuracy, precision, recall, F-measure, and AUC were obtained at 0.87, 0.88, 0.82, 0.85, and 0.87, respectively.

Conclusion: Algorithms for machine learning provide insightful information for anticipating challenging airway management. By making it possible to forecast airway difficulties more accurately, these techniques can potentially improve clinical practice and patient outcomes.

解开插管挑战:结合多个预测参数的机器学习方法。
背景:为了在麻醉期间保护患者,困难的气道管理是一个严重的问题,需要仔细计划和实施。机器学习预测工具最近在医学中变得越来越普遍,经常超越更成熟的技术。本研究旨在利用机器学习技术来预测具有挑战性的气道管理参数。方法:本研究采用横断面法。伊朗Loghman Hakim和Shahid Labbafinezhad医院的Shahid Beheshti医学科学大学总共提供了622份记录供分析。利用树的森林方法和特征重要性,选择重要特征。使用合成少数过采样技术(SMOTE)和重复编辑的最近邻欠采样来平衡数据。使用Python和10倍交叉验证,评估了七种机器学习算法:逻辑回归,支持向量机(SVM),随机森林(INFORMATION-GAIN和GINI-INDEX),决策树和k -近邻(KNN)。使用F-measure、AUC、Recall、Accuracy、Specificity和Precision等指标来评估模型的性能。结果:从原来的32个特征中筛选出24个重要特征。欠采样策略产生了比SMOTE更好的结果。其中,KNN (Euclidean, Minkowski)算法的性能优于其他算法。准确度、精密度、召回率、f测量值和AUC的最高值分别为0.87、0.88、0.82、0.85和0.87。结论:机器学习算法为预测具有挑战性的气道管理提供了有见地的信息。通过更准确地预测气道困难,这些技术可以潜在地改善临床实践和患者预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Anesthesiology
BMC Anesthesiology ANESTHESIOLOGY-
CiteScore
3.50
自引率
4.50%
发文量
349
审稿时长
>12 weeks
期刊介绍: BMC Anesthesiology is an open access, peer-reviewed journal that considers articles on all aspects of anesthesiology, critical care, perioperative care and pain management, including clinical and experimental research into anesthetic mechanisms, administration and efficacy, technology and monitoring, and associated economic issues.
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