Uncovering hidden disease patterns by simulating clinical diagnostic processes.

Choosing a sequence of observations (often with stochastic outcomes) which maximizes the information gain from a system of interacting variables is essential for a wide range of problems in science and technology, such as clinical diagnostic problems. Here, we use a probabilistic model of diseases and signs/symptoms to simulate the effects of medical decisions on the quality of diagnosis by maximizing an appropriate objective function of the medical observations. The study provides a systematic way of proposing new medical tests, considering the significance of diseases and cost of the suggested observations. The efficacy of methods and role of the objective functions as well as initial signs/symptoms are examined by numerical simulations of the diagnostic process by exhaustive or Monte Carlo sampling algorithms.