Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Yuan Li, Shuang Song, Liying Zhu, Xiaorun Zhang, Yijiao Mou, Maoxing Lei, Wenjing Wang, Zhen Tao
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引用次数: 0

Abstract

Background: Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream infections. SAB exhibits a high recurrence rate and mortality rate, leading to numerous clinical treatment challenges. Particularly, since the outbreak of COVID-19, there has been a gradual increase in SAB patients, with a growing proportion of (Methicillin-resistant Staphylococcus aureus) MRSA infections. Therefore, we have constructed and validated a pediction model for recurrent SAB using machine learning. This model aids physicians in promptly assessing the condition and intervening proactively.

Methods: The patients data is sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database version 2.2. The patients were divided into training and testing datasets using a 7:3 random sampling ratio. The process of feature selection employed two methods: Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO). Prediction models were built using Extreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Model validation included Receiver Operating Characteristic (ROC) analysis, Decision Curve Analysis (DCA), and Precision-Recall Curve (PRC). We utilized SHAP (SHapley Additive exPlanations) values to demonstrate the significance of each feature and explain the XGBoost model.

Results: After screening, MRSA, PTT, RBC, RDW, Neutrophils_abs, Sodium, Calcium, Vancomycin concentration, MCHC, MCV, and Prognostic Nutritional Index(PNI) were selected as features for constructing the model. Through combined evaluation using ROC、 DCA and PRC, XGBoost demonstrated the best predictive performance, achieving an AUC value of 0.76 (95% CI: 0.66-0.85) in ROC and 0.56 (95% CI: 0.37-0.75) in PRC. Building a website based on the Xgboost model. SHAP illustrated the feature importance ranking in the XGBoost model and provided examples to explain the XGBoost model.

Conclusions: The adoption of XGBoost for model development holds widespread acceptance in the medical domain. The prediction model for recurrent SAB, developed by our team, aids physicians in timely diagnosis and treatment of patients.

基于机器学习的复发性金黄色葡萄球菌菌血症患者预测模型。
背景:金黄色葡萄球菌菌血症(SAB)仍然是社区获得性和卫生保健相关血流感染的重要贡献者。SAB表现出高复发率和死亡率,导致许多临床治疗挑战。特别是,自2019冠状病毒病爆发以来,SAB患者逐渐增加,(耐甲氧西林金黄色葡萄球菌)MRSA感染的比例越来越大。因此,我们使用机器学习构建并验证了复发性SAB的修复模型。这种模式有助于医生及时评估病情并主动干预。方法:患者数据来源于重症监护医学信息市场(MIMIC-IV)数据库2.2版。采用7:3的随机抽样比例将患者分为训练数据集和测试数据集。特征选择过程采用了递归特征消除(RFE)和最小绝对收缩和选择算子(LASSO)两种方法。采用极端梯度增强(XGBoost)、随机森林(RF)、逻辑回归(LR)、支持向量机(SVM)和人工神经网络(ANN)建立预测模型。模型验证包括受试者工作特征分析(ROC)、决策曲线分析(DCA)和查准率-查全率曲线(PRC)。我们使用SHAP (SHapley Additive explanation)值来证明每个特征的重要性,并解释XGBoost模型。结果:筛选后选择MRSA、PTT、RBC、RDW、Neutrophils_abs、钠、钙、万古霉素浓度、MCHC、MCV、预后营养指数(PNI)作为构建模型的特征。通过ROC、DCA和PRC的综合评价,XGBoost表现出最佳的预测性能,ROC的AUC值为0.76 (95% CI: 0.66-0.85), PRC的AUC值为0.56 (95% CI: 0.37-0.75)。基于Xgboost模型构建网站。SHAP说明了XGBoost模型中的特性重要性排序,并提供了示例来解释XGBoost模型。结论:采用XGBoost进行模型开发在医学领域被广泛接受。我们团队开发的复发性SAB预测模型有助于医生及时诊断和治疗患者。
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
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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