A method for determining a risk of decompensated heart failure in a user includes receiving a first set of data that is fixed with respect to time. A machine-learning model generates one or more initial risk factors based on the first set of data. A second set of data for the user that dynamically updates over time is received from a wearable cardiovascular physiology monitor. The machine-learning model is used to generate dynamic data classifiers based on the one or more initial risk factors. Aggregate risk scores for the user are then indicated based on an evaluation of the second set of data against the dynamic data classifiers. In this way, static electronic medical records may be combined with dynamic, real-time data from wearable cardiovascular physiology monitors to provide an accurate and continuously updating risk of decompensated heart failure for a user.