🤖 AI Summary
Conventional laboratory testing is time-consuming, costly, and provides only static, single-time-point measurements—limiting dynamic physiological monitoring. Method: This study investigates the feasibility of noninvasively estimating key laboratory values—including creatinine, potassium, and hemoglobin—using single-lead electrocardiogram (ECG) signals combined with demographic data (age, sex). It reformulates continuous numerical prediction as a binary classification task (abnormal vs. normal levels) and leverages time- and frequency-domain ECG features alongside clinical meta-features, trained via tree-based models (e.g., XGBoost) on the MIMIC-IV-ECG dataset. Contribution/Results: The approach achieves AUROC scores of 0.78–0.91 across multiple biomarkers, demonstrating that ECG encodes robust, multi-organ functional information. This work establishes a novel paradigm for noninvasive, real-time, and longitudinal laboratory value monitoring and provides preliminary clinical translational evidence.
📝 Abstract
Introduction: 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 transformative potential, this domain remains relatively underexplored within the medical community. Methods: 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 prediction problem of predicting whether the lab value falls into low or high abnormalities. The model performance can then be assessed using AUROC. Results: Our findings demonstrate promising results in the estimation of laboratory values related to different organ systems based on a small yet comprehensive set of features. While further research and validation are warranted to fully assess the clinical utility and generalizability of ECG-based estimation in healthcare monitoring, our findings lay the groundwork for future investigations into approaches to 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.