Abstract: An individualized machine learning model embedded on a mobile health application, coupled to autonomous workflow agents systems to operate as a small artificial intelligence program. The machine learning algorithm learns from small datasets—making it possible to compute real-time data even on a low-grade smartphone device. It predicts early worsening outcomes by extracting patterns (features) that correlate with the individual baseline changes in heart failure status. The workflow agents support dynamic machine learning model deployment to different devices and configurations for real-time prediction and intervention. The different device configurations include patients who own only a smartphone device, patients who own a smartphone and wearable/biosensors, and patients who own a smartphone and who have an implanted cardiac device.