The present application relates to systems and methods for non-invasively determining at least one of left ventricular end diastolic pressure (LVEDP) or pulmonary capillary wedge pressure (PCWP) in a subject's heart, comprising: receiving, by a computer, a plurality of signals from a plurality of non-invasive sensors that measure a plurality of physiological effects that are correlated with functioning of said subject's heart, said plurality of physiological effects including at least one signal correlated with left ventricular blood pressure and at least one signal correlated with timing of heartbeat cycles of said subject's heart； training a machine learning model on said computer using said plurality of signals for periods of time in which said plurality of signals were being generated during a heart failure event of said subject's heart； determining said LVEDP or PCWP using said machine learning model at a time subsequent to said training and subsequent to said heart failure event.
