We’ve shown how AI and ML help diagnose and manage heart problems. New research has recently shown that a new ML model can improve ECG data interpretation, reducing misdiagnosis and optimizing patient monitoring data.
A further step is to look at monitoring and early disease detection while patients are being monitored within the home space. This is something our recent insight article has touched on.
By using traditional signal processing and ML, this model can overcome challenges caused by noisy data. They evaluated it using real-world single-lead ECG data from acutely ill patients receiving care at home as part of a ‘home hospital’ program. The model showed excellent performance, with a sensitivity of 98%, a specificity of 100%, an accuracy of 98%, a positive predictive value of 100%, and a negative predictive value of 93%.
It was effective in interpreting telemetry feeds for acutely ill patients. The current systems can detect atrial fibrillation with 92% accuracy and paroxysmal supraventricular tachycardia with 93% accuracy. However, the new ML algorithm could enhance arrhythmia identification.
The ML model can monitor chronically ill patients during medication changes or diagnose hidden arrhythmia. It could form the basis of a system that consistently monitors cardiac health through arrhythmia detection.
The study evaluated a single-lead ECG hybrid ML algorithm using data from acutely ill patients within a ‘home hospital’ programme. It shows the potential of AI and ML to enhance healthcare delivery and patient outcomes, particularly in cardiology. Investigating the impact of long-term arrhythmia burden on health outcomes is crucial for future research.
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