Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Luca Bernecker, Liv-Hege Johnsen, Torgil Riise Vangberg
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引用次数: 0

Abstract

Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls restricting blood flow. Early detection of ICAS is crucial to prevent serious consequences such as stroke. Here we apply three different machine learning methods, such as support vector machines, multi-layer perceptrons and Kolmogorov-Arnold Networks to predict ICAS according to sparse risk factors from blood lipids and demographic data, including smoking habits, age, sex, diabetes, blood pressure lowering and cholesterol-lowering drugs and high-density lipoprotein. We achieved similar performance on classification compared to modern detection algorithms for ICAS in TOF-MRA (time-of-flight magnetic resonance angiography). The prevalence of ICAS in the population is relatively low, which is often case in medicine. While in the medical research community, the issue of low prevalence is established, machine learning-based research in medicine often does not take into account a critical viewpoint of the prevalence in clinical settings of their methods. We showed that with a balanced training/test set an accuracy up to 81% was achievable, while with the inclusion of prevalence, the positive predictive value was at 19% to the prevalence data, changes the performance metrics. Therefore, we highlighted the discrepancy that can arise between the results reported by the models and their clinical relevance. Furthermore, the results demonstrate the predictive potential of limited risk factors, highlighting its potential contribution to a multi-modular classification algorithm based on MRAs.

在特罗姆瑟研究中,使用一小组危险因素预测颅内狭窄。
颅内动脉粥样硬化性狭窄(Intracranial atherosclerosis stenosis, ICAS)是指由于血管壁内部的斑块堆积限制血液流动而导致颅内动脉狭窄。早期发现ICAS对于预防中风等严重后果至关重要。在这里,我们应用了三种不同的机器学习方法,如支持向量机、多层感知器和Kolmogorov-Arnold网络,根据来自血脂和人口统计数据的稀疏危险因素(包括吸烟习惯、年龄、性别、糖尿病、降血压和降胆固醇药物以及高密度脂蛋白)来预测ICAS。与TOF-MRA(飞行时间磁共振血管成像)中ICAS的现代检测算法相比,我们在分类上取得了相似的性能。ICAS在人群中的患病率相对较低,这在医学中经常出现。虽然在医学研究界,确立了低流行率的问题,但基于机器学习的医学研究往往没有考虑到其方法在临床环境中流行的批判性观点。我们发现,使用平衡的训练/测试集可以实现高达81%的准确率,而在包含患病率的情况下,患病率数据的阳性预测值为19%,这改变了性能指标。因此,我们强调了模型报告的结果与其临床相关性之间可能出现的差异。此外,研究结果显示了有限风险因素的预测潜力,突出了其对基于MRAs的多模块分类算法的潜在贡献。
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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