Probabilistic detection of vital sign abnormality with Gaussian process regression

Vital-sign monitoring of patients within a hospital setting is a big component in the recognition and treatment of early signs of deterioration. Current vital-sign monitoring systems, including both manual early warning systems, and more sophisticated data fusion systems, typically make use of the most recently recorded data, and are unable to deal with missing data in a principled manner. The latter is particularly pertinent in the field of ambulatory monitoring, in which patient movement can result in sensor disconnections and other artefact. This paper presents a Gaussian process regression technique for estimating missing data and how it can be incorporated within an automated data fusion monitoring system. The technique is then demonstrated using vital-sign data from a recent clinical study conducted at the John Radcliffe Hospital, Oxford, showing an improvement over an existing data fusion algorithm by providing both an estimate of missing vital sign data and the uncertainty in the estimated value.