Method for predicting an evolution of a patient's heart-related condition

A computer-implemented method for predicting an evolution of at least one heart-related condition of a patient, the method including, for each heart-related condition:

• a) based on a set of inputs, at least part of the set of inputs being representative of a temporal evolution of a corresponding health variable among a predetermined set of health variables of the patient, computing a set of transformed variables, the set of transformed variables being a representation of the set of inputs in a predetermined latent data representation space;
• b) determining, based on the computed set of transformed variables, at least one index value, each representative of a respective risk of an unfavorable outcome of the heart-related condition;
• wherein determining at least one index value includes using a prediction model to determine the index value, the prediction model being trained based on a second training dataset including at least one set of reference transformed variables, each set of reference transformed variables being associated to a respective label indicating an evolution, over time, of a corresponding heart-related condition of a respective second reference subject.

A prediction system for predicting an evolution of at least one heart-related condition of a patient, the prediction system including a processor configured to, for each heart-related condition:

• a) compute a set of transformed variables based on a set of inputs, at least part of the set of inputs being representative of a temporal evolution of a corresponding health variable among a predetermined set of health variables of the patient, the set of transformed variables being a representation of the set of inputs in a predetermined latent data representation space;
• b) determine at least one index value based on the computed set of transformed variables, each index value being representative of a respective risk of an unfavorable outcome of the heart-related condition
• wherein determining at least one index value includes using a prediction model to determine the index value, the prediction model being trained based on a second training dataset including at least one set of reference transformed variables, each set of reference transformed variables being associated to a respective label indicating an evolution, over time, of a corresponding heart-related condition of a respective second reference subject.