4 Standard Data Mining Runs

To assemble data mining counts and statistics that can be compared over time, AERS Signal Management includes 22 data mining runs. All runs are refreshed as part of preparing a quarterly Signal Management update.

All of the standard data mining runs for Signal Management are MGPS runs that are limited to the set of monitored drugs. These runs are assigned to the project referenced in the signal configuration. The default name of this project is AERS Signal Mgmt.

If your user role includes access to the Data Mining Results tab in Oracle Empirica Signal, you can view complete run details for any run. Click the Data Mining Results tab, select the data mining run from the drop-down list, and then click View Run Details.

The following types of runs are included:

• Data Mining Runs Producing Disproportionality Scores
  Oracle Empirica Signal performs the following data mining runs to calculate disproportionality scores for drug-event terms for various subpopulations. Signal executes these runs on the data configuration specified for Data configuration for 2D runs in the signal configuration, and names the runs in accordance with that data configuration.
• Data Mining Runs for New or Changed Cases
  The cumulative subset data mining runs identify new and significantly changed reports relative to prior time periods. Oracle Empirica Signal executes these runs on the Data configuration for 2D runs from the signal configuration, and names the runs in accordance with that Data configuration.
• Data Mining Runs for SOC and HLT Scores
  Oracle Empirica Signal performs the following data mining runs to compute disproportionality scores at the SOC and HLT levels of the MedDRA event hierarchy for All reports and for US reports. Signal executes these runs on the Data configuration for 2D runs from the signal configuration, and names them in accordance with that Data configuration.
• Data Mining Run for Viewing Interactions
  Oracle Empirica Signal executes a three-dimensional MGPS data mining run to investigate Drug-Drug-Event interactions between reports that may merit further investigation.