Key result
The proposed domain-inspired AI clinical decision support system using a Random Forest classifier achieved high F1 scores (e.g., 98.30% for normal beats and 91.09% for ventricular beats) for arrhythmia classification, outperforming previous methods while remaining lightweight and interpretable.
Why the study?
AI-enabled real-time bedside monitoring and alerting from ECGs of acute patients can help improve clinical outcomes, motivating the development of a lightweight, interpretable tool for practical healthcare settings.
A lightweight, interpretable AI-based clinical decision support system using domain-inspired ECG features can effectively classify arrhythmias, facilitating potential real-time bedside monitoring.
May support interpretable arrhythmia tools in resource-limited settings; leaves open prospective clinical validation before practice change.
Cardiovascular diseases are one of the largest chronic killers in the modern world. AI enabled real time bedside monitoring and alerting from the electrocardiogram (ECG) of acute patients can help improve clinical outcome. We present a AI based clinical decision support system (CDSS) that extracts and presents domain-inspired features from ECG in real time. It also raises an alarm in an anomaly detection setting. Furthermore, we demonstrate that the system can also produce a diagnosis to assist the doctor connected to the clinically relevant features. We use arrhythmia as a test case. Given that the purpose of the paper is to develop a simple yet effective multimedia tool that can be applied in a practical healthcare setting, we have purposefully selected a non deep learning model. This is to make the CDSS lightweight and interpretable to increase the chances of future clinical translation.
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Sarkar et al. (2025) studied Arrhythmia (n=79). Domain-inspired AI-based clinical decision support system (Random Forest) vs. Previous machine learning and deep learning methods was evaluated on Arrhythmia classification performance (F1 score). The proposed domain-inspired AI clinical decision support system using a Random Forest classifier achieved high F1 scores (e.g., 98.30% for normal beats and 91.09% for ventricular beats) for arrhythmia classification, outperforming previous methods while remaining lightweight and interpretable.
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