Monitoring renal transplants: an application of the multiprocess Kalman filter

The multiprocess Kalman filter offers a powerful general framework for the modelling and analysis of noisy time series which are subject to abrupt changes in pattern. It has considerable potential application to many forms of biological series used in clinical monitoring. In particular, the approach can be used to provide on-line probabilities of whether changes have occurred, as well as to identify the type of change that is involved. In this paper, we extend and illustrate the methodology within the context of a particular case study. The general features of the problem, and the approach adopted, will be seen to have wide application.