The present state of trend detection and prediction in patient monitoring

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Abstract
An investigation has been carried out into the suitability of the following techniques for trend detection and forecasting in patient monitoring: Cusum; Trigg's Tracking Signal; The Patient Condition Factor; The Patient Alarm Warning System; Box-Jenkins models and the Harrison-Stevens Bayesian approach. The latter holds considerable promise since it is flexible and can be implemented on a microprocessor. Consideration has also been given to the need for a better knowledge of the statistical properties of the variables to be monitored and the problems of combining trends detected in severable variables.