Informatica

A method for counterfactual explanation of machine learning survival models is proposed. One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine learning survival model in the form of survival functions. A condition that establishes the difference between survival functions of the original example and the counterfactual is introduced. This condition is based on using a distance between mean times to event. It is shown that the counterfactual explanation problem can be reduced to a standard convex optimization problem with linear constraints when the explained black-box model is the Cox model. For other black-box models, it is proposed to apply the well-known Particle Swarm Optimization algorithm. Numerical experiments with real and synthetic data demonstrate the proposed method.

This paper offers an analysis of HIV/AIDS dynamics, defined by CD4 levels and Viral load, carried out from a macroscopic point of view by means of a general stochastic model. The model focuses on the patient's age as a relevant factor to forecast the transitions among the different levels of seriousness of the disease and simultaneously on the chronological time. The third model considers the two previous features simultaneously. In this way it is possible to quantify the medical scientific progresses due to the advances in the treatment of the HIV. The analyses have been performed through non-homogeneous semi-Markov processes. These models have been implemented by using real data provided by ISS (Istituto Superiore di Sanità, Rome, Italy). They refer to 2159 subjects enrolled in Italian public structures from September 1983 to January 2006. The relevant results include also the survival analysis of the infected patients. The computed conditional probabilities show the different responses of the subjects depending on their ages and the elapsing of time.