Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

IF 2.8 3区 医学 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Haobo Kong, Yong Li, Ya Shen, Jingjing Pan, Min Liang, Zhi Geng, Yanbei Zhang
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引用次数: 0

Abstract

Background: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

Methods: Data were collected from two centers and categorized into development and validation cohorts. Using the development cohort, candidate variables were selected via the Recursive Feature Elimination (RFE) method. Five machine learning algorithms, logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and support vector machine (SVM), were utilized to construct the predictive models. Model performance was evaluated through nested cross-validation and area under the curve (AUC) metrics, supplemented by interpretations using Shapley Additive explanations (SHAP) and line charts of AUC values. Models were subjected to external validation using an independent validation group, facilitating the early identification and management of pulmonary embolism risks in tuberculosis patients.

Results: Data from 694 patients were used for model development, and 236 patients from the validation group met the enrollment criteria. The optimal subset of variables identified included D-dimer, smoking status, dyspnea, age, sex, diabetes, platelet count, cough, fibrinogen, hemoglobin, hemoptysis, hypertension, chronic obstructive pulmonary disease (COPD), and chest pain. The RF model outperformed others, achieving an AUC of 0.839 (95% CI 0.780-0.899) and maintaining the highest average performance in external fivefold cross-validation (AUC: 0.906 ± 0.041).

Conclusions: The RF model demonstrates high and consistent effectiveness in predicting pulmonary embolism risk in tuberculosis patients.

使用机器学习算法预测肺结核患者肺栓塞的风险。
背景:本研究旨在利用机器学习算法开发具有强大泛化能力的预测模型,以评估结核病患者肺栓塞的风险。方法:从两个研究中心收集数据,分为开发组和验证组。利用开发队列,通过递归特征消除(RFE)方法选择候选变量。利用逻辑回归(LR)、随机森林(RF)、极端梯度增强(XGBoost)、轻梯度增强机(LightGBM)和支持向量机(SVM)五种机器学习算法构建预测模型。通过嵌套交叉验证和曲线下面积(AUC)指标评估模型性能,并辅以Shapley加性解释(SHAP)和AUC值折线图的解释。采用独立验证组对模型进行外部验证,有利于肺结核患者肺栓塞风险的早期识别和管理。结果:694例患者的数据用于模型开发,验证组中有236例患者符合入组标准。确定的最佳变量子集包括d -二聚体、吸烟状况、呼吸困难、年龄、性别、糖尿病、血小板计数、咳嗽、纤维蛋白原、血红蛋白、咯血、高血压、慢性阻塞性肺疾病(COPD)和胸痛。RF模型表现优异,AUC为0.839 (95% CI 0.780-0.899),在外部五重交叉验证中保持最高的平均性能(AUC: 0.906±0.041)。结论:射频模型在预测肺结核患者肺栓塞风险方面表现出高度和一致的有效性。
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
European Journal of Medical Research
European Journal of Medical Research 医学-医学:研究与实验
CiteScore
3.20
自引率
0.00%
发文量
247
审稿时长
>12 weeks
期刊介绍: European Journal of Medical Research publishes translational and clinical research of international interest across all medical disciplines, enabling clinicians and other researchers to learn about developments and innovations within these disciplines and across the boundaries between disciplines. The journal publishes high quality research and reviews and aims to ensure that the results of all well-conducted research are published, regardless of their outcome.
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