Artificial intelligence-based model for automatic real-time and noninvasive estimation of blood potassium levels in pediatric patients.

IF 0.9 Q4 CARDIAC & CARDIOVASCULAR SYSTEMS Annals of Pediatric Cardiology Pub Date : 2024-03-01 Epub Date: 2024-07-20 DOI:10.4103/apc.apc_54_24
Hamid Mokhtari Torshizi, Negar Omidi, Mohammad Rafie Khorgami, Razieh Jamali, Mohsen Ahmadi
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Abstract

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively.

Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results.

Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3.

Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios.

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基于人工智能的儿科患者血钾水平实时无创自动估算模型。
背景:血液电解质(如钾)的异常变化是导致重症监护病房患儿死亡的原因之一。对血清钾水平进行连续、实时监测可预防致命性心律失常,但目前还不现实。本研究旨在利用机器学习,以无创方式实时准确地估算血钾水平。方法:2021 年 12 月至 2022 年 6 月期间,招募了 Rajaie心脏病学和医学研究中心儿科及德黑兰心脏中心的住院患者。评估了患者的心电图(ECG)特征。我们为每个信号定义了 16 个特征,并自动提取了这些特征。在相关矩阵的帮助下进行了降维操作。我们使用线性回归、多项式、决策树、随机森林和支持向量机算法来寻找特征与血清钾水平之间的关系。最后,我们使用散点图和均方误差(MSE)来显示结果:在住院的 463 名患者(平均年龄:8 ± 1 岁;56% 为男孩)中,有 428 名患者符合纳入标准,其中 35 名患者的心电图噪声较高,被排除在外。经过降维步骤后,从每个心电信号中选取了 11 个特征。随机森林回归算法表现最佳,MSE 为 0.3:结论:使用机器学习算法可以根据心电信号准确估计血清钾水平。这可能有助于预测特定临床场景中的血清钾水平。
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来源期刊
Annals of Pediatric Cardiology
Annals of Pediatric Cardiology CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
1.40
自引率
14.30%
发文量
51
审稿时长
23 weeks
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