Learning diagnosis profiles through semi-supervised gradient descent of hidden Markov models

In this paper, we consider the problem of adapting the model of a diagnosis-helping module, which interacts with human experts. The approach consists of enforcing strong semantics in the model, so that this interaction may be as intuitive as possible. When learning the model, the problem consists in respecting these semantics while learning with few data. We addressed this problem through a semisupervised gradient descent algorithm applied to partially observable Markov models with fuzzy observations. This method optimizes several criteria at once, guiding the search to a compromise between the expert´s directives and objective evaluations. This method has been successfully applied to a tele-medicine application where the system monitors dialyzed patients and alerts nephrologists.