alarm: ALARM Monitoring System (synthetic) data set

In jtrecenti/bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Description

The ALARM ("A Logical Alarm Reduction Mechanism") is a Bayesian network designed to provide an alarm message system for patient monitoring.

Usage

data(alarm)

Format

The alarm data set contains the following 37 variables:

• CVP (central venous pressure): a three-level factor with levels LOW, NORMAL and HIGH.

• PCWP (pulmonary capillary wedge pressure): a three-level factor with levels LOW, NORMAL and HIGH.

• HIST (history): a two-level factor with levels TRUE and FALSE.

• TPR (total peripheral resistance): a three-level factor with levels LOW, NORMAL and HIGH.

• BP (blood pressure): a three-level factor with levels LOW, NORMAL and HIGH.

• CO (cardiac output): a three-level factor with levels LOW, NORMAL and HIGH.

• HRBP (heart rate / blood pressure): a three-level factor with levels LOW, NORMAL and HIGH.

• HREK (heart rate measured by an EKG monitor): a three-level factor with levels LOW, NORMAL and HIGH.

• HRSA (heart rate / oxygen saturation): a three-level factor with levels LOW, NORMAL and HIGH.

• PAP (pulmonary artery pressure): a three-level factor with levels LOW, NORMAL and HIGH.

• SAO2 (arterial oxygen saturation): a three-level factor with levels LOW, NORMAL and HIGH.

• FIO2 (fraction of inspired oxygen): a two-level factor with levels LOW and NORMAL.

• PRSS (breathing pressure): a four-level factor with levels ZERO, LOW, NORMAL and HIGH.

• ECO2 (expelled CO2): a four-level factor with levels ZERO, LOW, NORMAL and HIGH.

• MINV (minimum volume): a four-level factor with levels ZERO, LOW, NORMAL and HIGH.

• MVS (minimum volume set): a three-level factor with levels LOW, NORMAL and HIGH.

• HYP (hypovolemia): a two-level factor with levels TRUE and FALSE.

• LVF (left ventricular failure): a two-level factor with levels TRUE and FALSE.

• APL (anaphylaxis): a two-level factor with levels TRUE and FALSE.

• ANES (insufficient anesthesia/analgesia): a two-level factor with levels TRUE and FALSE.

• PMB (pulmonary embolus): a two-level factor with levels TRUE and FALSE.

• INT (intubation): a three-level factor with levels NORMAL, ESOPHAGEAL and ONESIDED.

• KINK (kinked tube): a two-level factor with levels TRUE and FALSE.

• DISC (disconnection): a two-level factor with levels TRUE and FALSE.

• LVV (left ventricular end-diastolic volume): a three-level factor with levels LOW, NORMAL and HIGH.

• STKV (stroke volume): a three-level factor with levels LOW, NORMAL and HIGH.

• CCHL (catecholamine): a two-level factor with levels NORMAL and HIGH.

• ERLO (error low output): a two-level factor with levels TRUE and FALSE.

• HR (heart rate): a three-level factor with levels LOW, NORMAL and HIGH.

• ERCA (electrocauter): a two-level factor with levels TRUE and FALSE.

• SHNT (shunt): a two-level factor with levels NORMAL and HIGH.

• PVS (pulmonary venous oxygen saturation): a three-level factor with levels LOW, NORMAL and HIGH.

• ACO2 (arterial CO2): a three-level factor with levels LOW, NORMAL and HIGH.

• VALV (pulmonary alveoli ventilation): a four-level factor with levels ZERO, LOW, NORMAL and HIGH.

• VLNG (lung ventilation): a four-level factor with levels ZERO, LOW, NORMAL and HIGH.

• VTUB (ventilation tube): a four-level factor with levels ZERO, LOW, NORMAL and HIGH.

• VMCH (ventilation machine): a four-level factor with levels ZERO, LOW, NORMAL and HIGH.

Note

The complete BN can be downloaded from http://www.bnlearn.com/bnrepository.

Source

Beinlich I, Suermondt HJ, Chavez RM, Cooper GF (1989). "The ALARM Monitoring System: A Case Study with Two Probabilistic Inference Techniques for Belief Networks." In "Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine", pp. 247-256. Springer-Verlag.

Elidan G (2001). "Bayesian Network Repository".
http://www.cs.huji.ac.il/site/labs/compbio/Repository.

Examples

# load the data and build the correct network from the model string.
data(alarm)
res = empty.graph(names(alarm))
modelstring(res) = paste("[HIST|LVF][CVP|LVV][PCWP|LVV][HYP][LVV|HYP:LVF]",
  "[LVF][STKV|HYP:LVF][ERLO][HRBP|ERLO:HR][HREK|ERCA:HR][ERCA]",
  "[HRSA|ERCA:HR][ANES][APL][TPR|APL][ECO2|ACO2:VLNG][KINK]",
  "[MINV|INT:VLNG][FIO2][PVS|FIO2:VALV][SAO2|PVS:SHNT][PAP|PMB][PMB]",
  "[SHNT|INT:PMB][INT][PRSS|INT:KINK:VTUB][DISC][MVS][VMCH|MVS]",
  "[VTUB|DISC:VMCH][VLNG|INT:KINK:VTUB][VALV|INT:VLNG][ACO2|VALV]",
  "[CCHL|ACO2:ANES:SAO2:TPR][HR|CCHL][CO|HR:STKV][BP|CO:TPR]", sep = "")
## Not run: 
# there are too many nodes for plot(), use graphviz.plot().
graphviz.plot(res)
## End(Not run)

jtrecenti/bnlearn documentation built on May 20, 2019, 3:16 a.m.