Explicit temporal models for decision–theoretic planning of clinical management

The management of patients over a prolonged period of time is a complicated task involving both diagnostic and prognostic reasoning with incomplete and often uncertain knowledge. Various formalisations of this type of task exist, but these often conceal one or more essential ingredients of the problem. This article explores the suitability of partially observable Markov decision processes to formalising the planning of clinical management. These processes allow for explicit representation of clinical states of the patient, the management strategy employed, the objectives of treatment, and the role of time and change in reasoning. However, practical application is hampered by their coarse representational granularity and complex formulation. It is discussed how probabilistic network representations can be used to alleviate these obstacles. The resulting method is illustrated with a real-world example from the domain of paediatric cardiology.