Planning treatment of ischemic heart disease with partially observable Markov decision processes

Diagnosis of a disease and its treatment are not separate, one-shot activit ies. Instead, they are very often dependent and interleaved over time. This is mostly due to uncertainty about the underlying disease, uncertainty ass ociated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framewo rk of partially observable Markov decision processes (POMDPs) developed and used in the operations research, control theory and artificial intelligenc e communities is particularly suitable for modeling such a complex decision process. In this paper, we show how the POMDP framework can be used to mod el and solve the problem of the management of patients with ischemic heart disease (IHD), and demonstrate the modeling advantages of the framework ove r standard decision formalisms. (C) 2000 Elsevier Science B.V. All rights r eserved.