Characteristics of patient influencing disease progession

A system comprising:

• a processor coupled to a memory, the memory having recorded thereon a computer program for determining characteristics of a patient that influence a progression of a disease of the patient that when executed by the processor causes the processor to be configured to:
• provide a deep learning neural network modeling a multi-state model of the disease progression having states and transitions between the states, the multi-state model outputting transition-specific probabilities during a follow-up period,
• provide a dataset of multi-state time-to-event data of a set of patients, the dataset comprising, for each patient of the set, a real value of each characteristic of a set of characteristics,
• while training the neural network with the provided dataset, for each transition of the multi-state model and for each characteristic, determine a respective quantification of an impact of the characteristic on results of the neural network, the determined respective quantification being a sum, for each patient, of a difference between a conditional probability obtained for a baseline value of the characteristic and a conditional probability obtained for the real value of the characteristic for the patient;
• for each transition:
• identify a list of characteristics of the set of characteristics, each characteristic of the list having a significant respective quantification for the transition, and
• for each given characteristic of the identified list, determine a relationship between the given characteristic and probabilities of transition based on computed predictions of conditional probabilities for each patient of the set of patients, the relationship being determined by fixing each characteristic of the patient to each value of the characteristic;
• and
• provide the identified lists and the determined relationships that influence the progression of the disease of the patient.

A computer-implemented method for determining characteristics of a patient that influence a progression of a disease of the patient, the method comprising:

• obtaining a dataset of multi-state time-to-event data of a set of patients, the dataset comprising, for each patient of the set, a real value of each characteristic of a set of characteristics;
• while training a neural network with the obtained dataset, for each transition of a multi-state model and for each characteristic, determining a respective quantification of an impact of the characteristic on results of a deep learning neural network modeling the multi-state model of the disease progression having states and transitions between the states, the multi-state model outputting transition-specific probabilities during a follow-up period, the determined respective quantification being a sum, for each patient, of a difference between a conditional probability obtained for a baseline value of the characteristic and a conditional probability obtained for the real value of the characteristic for the patient;
• for each transition:
• identifying a list of characteristics of the set of characteristics, each characteristic of the list having a significant respective quantification for the transition, and
• for each given characteristic of the identified list, determining a relationship between the given characteristic and probabilities of transition based on computed predictions of conditional probabilities for each patient of the set of patients, the relationship being determined by fixing each characteristic of the patient to each value of the characteristic;
• and
• outputting the identified lists and the determined relationships that influence the progression of the disease of the patient.