Trusted decision support system and method

Abstract

Methods and apparatus for providing a comprehensive decision support system to include predictions, recommendations with consequences and optimal follow-up actions in specific situations are described. Data is obtained from multiple disparate data sources, depending on the information deemed necessary for the situation being modeled. The decision support system provides a prediction or predictions and a recommendation or a choice of recommendations based on the correlative analysis and/or other analyses. Also described are methods and apparatus for developing application specific decision support models. The decision support model development process may include identifying multiple disparate data sources for retrieval of related information, selection of classification variables to be retrieved from the data sources, assignment of weights to each classification variable, selecting and/or defining rules, and selecting and/or defining analysis functions.

What is claimed is: 
     
         1 . A method comprising:
 selecting, by an electronic device comprising at least one processor, a data set for an application area;   creating, by the electronic device comprising at least one processor, a weighted data set based on weighted scores assigned to data in the data set;   generating, by the electronic device comprising at least one processor, a correlation between the weighted data set and one or more previously correlated weighted data sets; and   determining, by the electronic device comprising at least one processor, a recommended action as a response to an event related to the data set, and outcome information for the recommended action, wherein said determining is based at least in part upon the correlation between the weighted data set and one or more previously correlated weighted data sets.   
     
     
         2 . The method of  claim 1 , further comprising performing, by the electronic device comprising at least one processor, statistical analysis on the data set. 
     
     
         3 . The method of  claim 1 , wherein the weighted data set is created according to weighting guidelines that evolve and develop over time. 
     
     
         4 . The method of  claim 1 , further comprising performing statistical analysis on the data set, wherein statistical analysis performed on the data set is performed by a statistical analyses engine programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set. 
     
     
         5 . The method of  claim 4 , wherein the mathematical instructions comprise weights to be assigned to the data set according to at least one of a source from which the data set was received and an age of the data set. 
     
     
         6 . The method of  claim 4 , wherein the mathematical instructions comprise at least one of fuzzy logic instructions, Bayesian analyses instructions, neural network analyses instructions, probability calculation instructions, mean calculation instructions, confidence interval calculation instructions, Z-test instructions, T-test instructions, autoregressive modeling instructions, or residual analysis instructions for multiple regression. 
     
     
         7 . The method of  claim 1  wherein the data set for an application area comprises data from a sensor network. 
     
     
         8 . The method of  claim 7 , further comprising performing, by the electronic device comprising at least one processor, statistical analysis by a statistical analyses engine on the data set. 
     
     
         9 . The method of  claim 7  further comprising performing statistical analysis on the data set, wherein the statistical analyses engine is configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set. 
     
     
         10 . The method of  claim 9 , wherein the mathematical instructions comprise weights to be assigned to the data set according to at least one of a source from which the data set was received or an age of the data set. 
     
     
         11 . The method of  claim 9 , wherein the mathematical instructions comprise at least one of fuzzy logic instructions, Bayesian analyses instructions, neural network analyses instructions, probability calculation instructions, mean calculation instructions, confidence interval calculation instructions, Z-test instructions, T-test instructions, autoregressive modeling instructions, or residual analysis instructions for multiple regression. 
     
     
         12 . A method comprising:
 selecting, by an electronic device comprising at least one processor, a data set for an application area;   analyzing, by the electronic device comprising at least one processor, the data set based on fuzzy logic instructions;   generating, by the electronic device comprising at least one processor, a recommended action and outcome information for the recommended action; and   performing, by the electronic device comprising at least one processor, statistical analysis by a statistical analysis engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set, wherein the mathematical instructions comprise weights to be assigned to the data set according to at least one of a source from which the data set was received or an age of the data set.   
     
     
         13 . The method of  claim 12 , wherein the mathematical instructions comprise at least one of fuzzy logic instructions, Bayesian analyses instructions, neural network analyses instructions, probability calculation instructions, mean calculation instructions, confidence interval calculation instructions, Z-test instructions, T-test instructions, autoregressive modeling instructions, or residual analysis instructions for multiple regression. 
     
     
         14 . The method of  claim 12 , further comprising:
 receiving, by the electronic device comprising at least one processor, current data from at least one of a plurality of sources;   comparing, by the electronic device comprising at least one processor, the current data and the previously received data; and   providing, by the electronic device comprising at least one processor, a recommended action and outcome information for the recommended action based at least in part on the comparison.   
     
     
         15 . The method of  claim 14 , wherein the plurality of sources includes disparate sources. 
     
     
         16 . The method of  claim 14 , further comprising a database of previously recommended actions associated with the previously received data, wherein the electronic device is further configured to provide a recommended action and outcome information based at least in part on the current data, the previously received data, or the associated previously recommended actions. 
     
     
         17 . A method for performing an application specific decision support model comprising:
 identifying, by an electronic device comprising at least one processor, data sources for an application area;   selecting, by the electronic device comprising at least one processor, variables to be searched for in each identified data source;   assigning, by the electronic device comprising at least one processor, weights to each variable searched, wherein weights correspond to relevance of information in each data source;   identifying, by the electronic device comprising at least one processor, instructions to apply to a search of a selected variable in an identified data source;   conducting, by the electronic device comprising at least one processor, a correlation process, wherein a current scenario is correlated with previous scenarios;   conducting, by the electronic device comprising at least one processor, multiple analysis on the current scenario;   determining, by the electronic device comprising at least one processor, at least one next likely outcome or event for at least one time point;   identifying, by the electronic device comprising at least one processor, at least one recommendation for each time point, wherein the at least one recommendation is based on the at least one next likely outcome or event;   determining, by the electronic device comprising at least one processor, at least one potential consequence for each recommendation;   performing, by the electronic device comprising at least one processor, an action based upon the at least one recommendation and at least one potential consequence; and   storing, by the electronic device comprising at least one processor, results of the action.   
     
     
         18 . The method of  claim 17 , wherein the making of at least one prediction, the identifying of at least one recommendation, or the determining of at least one potential consequence are executed simultaneously. 
     
     
         19 . The method of  claim 17 , wherein the instructions comprise at least one of fuzzy logic instructions, Bayesian analyses instructions, neural network analyses instructions, probability calculation instructions, mean calculation instructions, confidence interval calculation instructions, Z-test instructions, T-test instructions, autoregressive modeling instructions, or residual analysis instructions for multiple regression. 
     
     
         20 . The method of  claim 17 , wherein the action performed comprises presenting the at least one recommendation and at least one consequence to a user for a user input.