Evaluating regression and probabilistic methods for ECG-based electrolyte prediction

Journal article

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 binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction—a method with high potential impact within multiple clinical scenarios.Title in the list of papers of Fredrik K. Gustafsson's thesis: ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods

Authors: von Bachmann, P; Gedon, D; Gustafsson, FK; Ribeiro, AH; Lampa, E

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Scientific Reports
• Volume: 14
• Issue: 1
• Pages: 15273-15273
• Publication date: 2024-07-03
• DOI: 10.1038/s41598-024-65223-w
• EISSN: 2045-2322
• ISSN: 2045-2322
• Language: English
• Keywords: Statistics; Binary classification; Data mining; Artificial intelligence; Linear regression; Computer science; Support vector machine; Binary number; Regression; Predictive modelling; Probabilistic logic; Mathematics; Artificial neural network; Regression analysis; Machine learning