Laboratory value represents a cornerstone of medical diagnostics, but suffers from slow turnaround times, and high costs and only provides information about a single point in time. The continuous estimation of laboratory values from non-invasive data such as electrocardiogram (ECG) would therefore mark a significant frontier in healthcare monitoring. Despite its potential, this domain remains relatively underexplored. In this preliminary study, we used a publicly available dataset (MIMIC-IV-ECG) to investigate the feasibility of inferring laboratory values from ECG features and patient demographics using tree-based models (XGBoost). We define the prediction task as a binary problem of whether the lab value falls into low or high abnormalities. We assessed model performance with AUROC. Our findings demonstrate promising results in the estimation of laboratory values related to different organ systems. While further research and validation are warranted to fully assess the clinical utility and generalizability of the approach, our findings lay the groundwork for future investigations for laboratory value estimation using ECG data. Such advancements hold promise for revolutionizing predictive healthcare applications, offering faster, non-invasive, and more affordable means of patient monitoring.

翻译：实验室指标是医学诊断的基石，但其存在周转时间慢、成本高且仅能反映单一时点信息的局限性。因此，从心电图（ECG）等非侵入性数据中持续估算实验室指标，将成为健康监测领域的重要前沿。尽管潜力巨大，该领域仍相对缺乏深入探索。在本初步研究中，我们利用公开数据集（MIMIC-IV-ECG），通过基于树的模型（XGBoost）探究了从心电图特征及患者人口统计学信息推断实验室指标的可行性。我们将预测任务定义为实验室指标是否落入低值或高值异常的二分类问题，并使用AUROC评估模型性能。研究结果表明，该方法在估算涉及不同器官系统的实验室指标方面展现出良好前景。尽管仍需进一步研究和验证以全面评估该方法的临床效用与泛化能力，但本研究为未来利用心电图数据估算实验室指标的研究奠定了基础。此类进展有望革新预测性医疗应用，为患者监测提供更快速、非侵入且更经济的技术手段。