Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features
<|reference_start|>Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features: Introduction: Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient's cardiac health and are readily available. Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions.   Methods: In our study, we used publicly available datasets (MIMIC-IV-ECG-ICD and ECG-VIEW II) to investigate the feasibility of inferring general diagnostic conditions from ECG features. To this end, we trained a tree-based model (XGBoost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions.   Results: Our results demonstrate the reliability of estimating 23 cardiac as well as 21 non-cardiac conditions above 0.7 AUROC in a statistically significant manner across a wide range of physiological categories. Our findings underscore the predictive potential of ECG data in identifying well-known cardiac conditions. However, even more striking, this research represents a pioneering effort in systematically expanding the scope of ECG-based diagnosis to conditions not traditionally associated with the cardiac system.<|reference_end|>
@article{alcaraz2024estimation,
    title={Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram
  Features},
    author={Juan Miguel Lopez Alcaraz, Nils Strodthoff},
    journal={arXiv preprint arXiv:2408.17329},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17329},
    primaryClass={eess.SP cs.LG}
}