评估用于老年骨关节炎机器学习预测模型的血液和尿液生物标记物：可行性研究

IF 4.9 2区医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer methods and programs in biomedicine Pub Date : 2025-04-14 DOI:10.1016/j.cmpb.2025.108779

Jun-hee Kim

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引用次数: 0

摘要

骨关节炎（OA）是一种常见的退行性关节疾病，尤其影响50岁以上的人群。它会降低老年人的生活质量，限制他们的身体活动。OA的早期诊断对于有效管理、减缓疾病进展和减轻症状至关重要。目的本研究评估了利用血液和尿液生物标志物建立老年OA诊断预测模型的可行性。此外，我们将衍生生物标志物模型与使用标准血液和尿液变量的模型进行比较，以评估衍生生物标志物对OA诊断的影响。方法分析10,743名参与者的数据，包括血液和尿液测试的变量。使用机器学习算法来开发模型。衍生的生物标志物是根据Shapley加性解释（SHAP）分析中突出的最重要特征来确定的。将基于血液和尿液生物标志物的模型与基于衍生生物标志物的模型的性能进行比较，并使用SHAP对重要变量进行分析。结果支持向量机在血液数据集上表现出最高的准确率（0.6245）和F1得分（0.6232），而随机森林模型在尿液数据集上表现最好（0.5770）。与使用所有血液和尿液变量的模型相比，衍生的生物标志物模型结合了表现最好的模型中高度重要的生物标志物，显示出更好的预测性能。衍生的生物标志物模型取得了最高的性能指标，逻辑回归算法的准确度为0.6450，精密度为0.6443，召回率为0.6450，F1得分为0.6430。结论：从常规血液和尿液检测中获得的生物标志物有望在老年OA患者的早期发现和全面诊断中发挥作用。这些生物标记物可用于临床，因为它们可以整合到常规检测中，有助于早期发现并改善患者预后。

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Evaluation of blood- and urine-derived biomarkers for machine learning prediction models of osteoarthritis in elderly patients: A feasibility study

Background

Osteoarthritis (OA) is a common degenerative joint disease, particularly affecting individuals aged >50 years. It deteriorates quality of life and restricts physical activity in the elderly. Early diagnosis of OA is crucial for effective management, slowing disease progression, and alleviating symptoms.

Objectives

This study evaluated the feasibility of utilizing biomarkers derived from blood and urine in developing predictive models for OA diagnosis in the elderly population. Additionally, we compared the derived biomarker model with a model using standard blood and urine variables to assess the impact of the derived biomarkers on OA diagnosis.

Methods

Data from 10,743 participants were analyzed, including variables from blood and urine tests. Machine learning algorithms were used to develop the models. Derived biomarkers were identified based on the most significant features highlighted by Shapley Additive exPlanations (SHAP) analysis. The performance of models based on blood and urine biomarkers was compared with that of models based on derived biomarkers, and important variables were analyzed using SHAP.

Results

The support vector machine demonstrated the highest accuracy (0.6245) and F1 score (0.6232) for the blood dataset, whereas the random forest model achieved the best performance (0.5770) for the urine dataset. The derived biomarker model, which combined biomarkers of high importance from the best-performing models, showed improved predictive performance compared with the model using all blood and urine variables. The derived biomarker model achieved the highest performance metrics, with the logistic regression algorithm yielding an accuracy of 0.6450, precision of 0.6443, recall of 0.6450, and F1 score of 0.6430.

Conclusions

Biomarkers derived from routinely available blood and urine tests show promise for the early detection and comprehensive diagnosis of OA in older patients. These biomarkers are practical for clinical use, as they can be integrated into routine testing, potentially aiding early detection and improving patient outcomes.

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来源期刊

Computer methods and programs in biomedicine 工程技术-工程：生物医学

CiteScore

12.30

自引率

6.60%

发文量

601

审稿时长

135 days

期刊介绍： To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.