Method and apparatus for multi-domain anomaly pattern definition and detection

Abstract

Disclosed herein is a multi-domain anomaly pattern definition and detection module. The module receives raw data from different kinds of anomalies from a variety of detection algorithms and generates scores associated with the data. If any scores exceed a threshold, the algorithm gathers further information such as counts or listings of detailed data for a geographic region. The detailed data can include emergency department and lab department data related to a particular health concern such as a respiratory syndrome. Summaries can identify anomalies and numbers of events according to geographic region and utilizing probability algorithms. Other databases such as animal data collected under the Department of Agriculture may also be utilized. The data is presented in a familiar form such as a map or a table such that a subject matter expert may determine whether to further investigate an anomaly as a potential risk, for example, a health risk.

1 . A method comprising:
 monitoring a plurality of events across multiple domains of biosurveillance activity;   analyzing the plurality of events to yield a multi-domain event analysis;   based on the multi-domain event analysis, detecting, via a processor, a plurality of anomalies comprising a subset of the plurality of events that exceeds a threshold; and   generating an alert based on the subset.   
     
     
         2 . The method of  claim 1 , wherein the multiple domains of biosurveillance activity comprise at least one of hospital data, environmental data, work absenteeism, medicine sales, doctor office visits, laboratory data, school attendance data, and medicare purchasing data. 
     
     
         3 . The method of  claim 1 , further comprising supplementing the subset based on a new event derived from at least one domain of biosurveillance activity. 
     
     
         4 . The method of  claim 1 , wherein analyzing the plurality of events comprises applying an algorithm to data associated with the plurality of events. 
     
     
         5 . The method of  claim 1 , wherein each of the plurality of events comprises at least one of an algorithm, a parameter, a score, a fact, a record, a report, and biosurveillance data. 
     
     
         6 . The method of  claim 1 , wherein detecting the plurality of anomalies comprises applying an algorithm to at least one of the multi-domain event analysis and the plurality of events. 
     
     
         7 . The method of  claim 1 , wherein analyzing the plurality of events comprises summarizing the plurality of events. 
     
     
         8 . A system comprising:
 a processor; and   a memory storing instructions for controlling the processor to perform steps comprising:
 monitoring a plurality of events across multiple domains of biosurveillance activity; 
 analyzing the plurality of events to yield a multi-domain event analysis; 
 based on the multi-domain event analysis, detecting a plurality of anomalies comprising a subset of the plurality of events that exceeds a threshold; and 
 generating an alert based on the subset. 
   
     
     
         9 . The system of  claim 8 , wherein the multiple domains of biosurveillance activity comprise at least one of hospital data, environmental data, work absenteeism, medicine sales, doctor office visits, laboratory data, school attendance data, and medicare purchasing data. 
     
     
         10 . The system of  claim 8 , further comprising supplementing the subset based on a new event derived from at least one domain of biosurveillance activity. 
     
     
         11 . The system of  claim 8 , wherein analyzing the plurality of events comprises applying an algorithm to data associated with the plurality of events. 
     
     
         12 . The system of  claim 8 , wherein each of the plurality of events comprises at least one of an algorithm, a parameter, a score, a fact, a record, a report, and biosurveillance data. 
     
     
         13 . The system of  claim 8 , wherein detecting the plurality of anomalies comprises applying an algorithm to at least one of the multi-domain event analysis and the plurality of events. 
     
     
         14 . The system of  claim 8 , wherein analyzing the plurality of events comprises summarizing the plurality of events. 
     
     
         15 . A non-transitory computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform steps comprising:
 monitoring a plurality of events across multiple domains of biosurveillance activity;   analyzing the plurality of events to yield a multi-domain event analysis;   based on the multi-domain event analysis, detecting a plurality of anomalies comprising a subset of the plurality of events that exceeds a threshold; and   generating an alert based on the subset.   
     
     
         16 . The non-transitory computer-readable storage medium of  claim 15 , wherein the multiple domains of biosurveillance activity comprise at least one of hospital data, environmental data, work absenteeism, medicine sales, doctor office visits, laboratory data, school attendance data, and medicare purchasing data. 
     
     
         17 . The non-transitory computer-readable storage medium of  claim 15 , further comprising supplementing the subset based on a new event derived from at least one domain of biosurveillance activity. 
     
     
         18 . The non-transitory computer-readable storage medium of  claim 15 , wherein analyzing the plurality of events comprises applying an algorithm to data associated with the plurality of events. 
     
     
         19 . The non-transitory computer-readable storage medium of  claim 15 , wherein each of the plurality of events comprises at least one of an algorithm, a parameter, a score, a fact, a record, a report, and biosurveillance data. 
     
     
         20 . The non-transitory computer-readable storage medium of  claim 15 , wherein detecting the plurality of anomalies comprises applying an algorithm to at least one of the multi-domain event analysis and the plurality of events.