Probabilistic patient monitoring using extreme value theory: A multivariate, multimodal methodology for detecting patient deterioration
Conventional patient monitoring is performed by generating alarms when vital signs exceed pie-determined thresholds, but the false-alarm rate of such monitors in hospitals is so high that alarms are typically ignored. We propose a principled, probabilistic method for combining vital signs into a mul...
| Main Authors: | , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
2010
|
| Summary: | Conventional patient monitoring is performed by generating alarms when vital signs exceed pie-determined thresholds, but the false-alarm rate of such monitors in hospitals is so high that alarms are typically ignored. We propose a principled, probabilistic method for combining vital signs into a multivariate model of patient state, using extreme value theory (EVT) to generate robust alarms if a patient's vital signs are deemed to have become sufficiently "extreme". Our proposed formulation operates many orders of magnitude faster than existing methods, allowing on-line learning of models, leading ultimately to patient-specific monitoring. |
|---|