Evaluating regression and probabilistic methods for ECG-based electrolyte prediction.

ABSTRACT: Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binar

SUBMITTER: von Bachmann P 

PROVIDER: S-EPMC11222546 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature