Key result
A novel complex event processing approach with automated statistical threshold computation demonstrated merits in speed, precision, and recall for heart failure prediction.
Why the study?
Does a complex event processing engine with statistical thresholding improve the speed, precision, and recall of heart failure prediction?
Does a complex event processing engine with statistical thresholding improve the speed, precision, and recall of heart failure prediction?
An automated, statistical threshold-based complex event processing engine can effectively process health data for heart failure prediction.
May support automated HF prediction tools; leaves open prospective clinical validation before adoption.
This paper presents a novel health analysis approach for heart failure prediction. It is based on the use of complex event processing (CEP) technology, combined with statistical approaches. A CEP engine processes incoming health data by executing threshold-based analysis rules. Instead of having to manually set up thresholds, our novel statistical algorithm automatically computes and updates thresholds according to recorded historical data. Experimental results demonstrate the merits of our approach in terms of speed, precision, and recall.
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Mdhaffar et al. (2017) studied Heart failure. Complex event processing (CEP) with statistical approaches was evaluated on Speed, precision, and recall. A novel complex event processing approach with automated statistical threshold computation demonstrated merits in speed, precision, and recall for heart failure prediction.
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