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Vital signs recorded at the hospital bedside manually by clinical staff are key indicators of patient physiology and may be used to track patient deterioration. The low frequency of vital-sign observations by clinical staff (every 4, 8 or 12 hours) makes it difficult to determine the underlying distribution for each vital sign. In this paper we demonstrate how a Bayesian approach may be used to estimate the unknown parameters of vital sign data.


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