A Markovian Decision Model for Clinical Diagnosis and Treatment Applied to the Respiratory System

The complex process of medical diagnosis has traditionally relied on the experience and judgement of the clinician. With the increased application of systems ideas to medical problems in general, it is timely to consider their application to the diagnostic situation. Decision theory is well established, and several decision models have been applied to medical problems. A unified approach to clinical decision making is presented. This approach combines partially observable Markov decision processes with cause-effect models as a probabilistic representation of the diagnostic process. This new class of model has a potential application to medical diagnosis and treatment, and a respiratory system example is presented. The methodology is given for combining the patient states of health, the clinician's state of knowledge of the cause-effect representation from the observation space, and finally the treatment decisions with which to restore the patient to a more desirable state of health.