Abstract: A computer-implemented method for determining characteristics of a patient that influence a progression of a disease of the patient. The method includes, while training a neural network with a provided dataset, for each transition of the multi-state model, and for each characteristic, determining a respective quantification of an impact of the characteristic on the results of the neural network. The method includes, for each transition, identifying a list of characteristics of the set of characteristics, and, for each given characteristic of the identified list, determining a relationship between the given characteristic and probabilities of transition. The method includes providing the identified lists and the determined relationships that influence the progression of the disease of the patient. Such a method forms an improved solution for determining patient's characteristics that influence patient disease progression.

Abstract: A computer-implemented method for determining characteristics of a patient that influence a progression of a disease of the patient. The method includes, while training a neural network with a provided dataset, for each transition of the multi-state model, and for each characteristic, determining a respective quantification of an impact of the characteristic on the results of the neural network. The method includes, for each transition, identifying a list of characteristics of the set of characteristics, and, for each given characteristic of the identified list, determining a relationship between the given characteristic and probabilities of transition. The method includes providing the identified lists and the determined relationships that influence the progression of the disease of the patient. Such a method forms an improved solution for determining patient's characteristics that influence patient disease progression.

Abstract: A computer-implemented method for machine-learning a function configured, based on input covariates representing medical characteristics of a patient with respect to a multi-state model of an illness having states and transitions between the states, to output a distribution of transition-specific probabilities for each interval of a set of intervals, the set of intervals forming a subdivision of a follow-up period. The machine-learning method including obtaining a dataset of covariates and time-to-event data of a set of patients, and training the function based on the dataset. This forms an improved solution for determining accurate patient data with respect to a multi-state model of an illness.