R: ALARM monitoring system (synthetic) data set

alarm {bnlearn} | R Documentation

## ALARM monitoring system (synthetic) data set

### 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". Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine, pp. 247–256.

### Examples

    # load the data.
    data(alarm)
    # create and plot the network structure.
    modelstring = paste0("[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]")
    dag = model2network(modelstring)
    ## Not run: graphviz.plot(dag)

[Package bnlearn version 4.4.1 Index]