| abstract |
Systems and methods for detecting a heart failure (HF) event indicative of worsening of HF, or for identifying patient at elevated risk of developing future HF event, are described. The system and methods can detect an HF event or predict HF risk using a multitude of fusion algorithms or classifiers, each employing one or more physiologic sensor signals. A system can comprise two or more partial predictor circuits each can adaptively generate a dynamic computational model (DCM). Each partial predictor circuit can determine a partial risk index indicating a likelihood of the patient developing a precursor physiologic event indicative or correlative of a future HF event. The system can include a prediction fusion circuit that can combine the partial risk indices and generate a composite risk indicator for detecting or predicting a likelihood of the patient developing a future HF event. |