Monitoring patient vital-sign deterioration trajectories using Bayesian inference
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 dist...
| Egile Nagusiak: | , , |
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| Formatua: | Conference item |
| Argitaratua: |
IEEE
2011
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| Gaia: | 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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