  We study a diagnostic strategy which is based on the anticipation of the
diagnostic process by simulation of the dynamical process starting from the
initial findings. We show that such a strategy could result in more accurate
diagnoses compared to a strategy that is solely based on the direct
implications of the initial observations. We demonstrate this by employing the
mean-field approximation of statistical physics to compute the posterior
disease probabilities for a given subset of observed signs (symptoms) in a
probabilistic model of signs and diseases. A Monte Carlo optimization algorithm
is then used to maximize an objective function of the sequence of observations,
which favors the more decisive observations resulting in more polarized disease
probabilities. We see how the observed signs change the nature of the
macroscopic (Gibbs) states of the sign and disease probability distributions.
The structure of these macroscopic states in the configuration space of the
variables affects the quality of any approximate inference algorithm (so the
diagnostic performance) which tries to estimate the sign/disease marginal
probabilities. In particular, we find that the simulation (or extrapolation) of
the diagnostic process is helpful when the disease landscape is not trivial and
the system undergoes a phase transition to an ordered phase.
