Intelligent decision support system

A system for implementing intelligent decision support for decision making in a computing system, comprising:

• one or more computers with executable instructions that when executed cause the system to:
• collect data about a plurality of historical data;
• generate one or more samples of annotated data by one or more domain experts;
• generate one or more machine learning models using different splits of the data and the annotated data to generate recommendations for one or more decisions;
• combine the one or more machine learning models to generate a recommendation for the one or more decisions using one or more ensemble machine learning models;
• generate one or more natural language reasons for the recommendations by one or more machine learning models and providing one or more reasons for the recommendation using a dialog system and
• perform each of the following upon the one or more machine learning models being an ensemble of decision trees:
• for each constituent decision tree model in the ensemble, determining a set of machine learning rules that were used in a path by a decision tree to arrive at an output recommendation;
• mapping each machine learning rule in the path by each of the constituent decision tree models to a natural language reason;
• for each data point for which the recommendations are being generated, creating a set of reasons by computing a union of a set of natural language reasons mapped from paths followed by a subset of the constituent decision tree models that participated in a final recommendation by the ensemble of decision trees for the data point;
• and
• for each of the data points for which recommendations are being generated, displaying the set of natural language reasons as the reasons for the recommendation by the ensemble machine learning model;
• and
• interact with a user via the dialog system and engaging in a dialog with the user in relation to the questions about the data and the recommendation.

A computer program product for, by a processor, implementing intelligent decision support for decision making in a computing system, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

• an executable portion that collects data about a plurality of historical data;
• an executable portion that generates one or more samples of annotated data by one or more domain experts;
• an executable portion that generates one or more machine learning models using different splits of the data and the annotated data to generate recommendations for one or more decisions;
• an executable portion that combines the one or more machine learning models to generate a recommendation for the one or more decisions using one or more ensemble machine learning models;
• an executable portion that generates one or more natural language reasons for the recommendations by one or more machine learning models and providing one or more reasons for the recommendation using a dialog system and
• an executable portion that performs each of the following upon the one or more machine learning models being an ensemble of decision trees:
• for each constituent decision tree model in the ensemble, determining a set of machine learning rules that were used in a path by a decision tree to arrive at an output recommendation;
• mapping each machine learning rule in the path by each of the constituent decision tree models to a natural language reason;
• for each data point for which the recommendations are being generated, creating a set of reasons by computing a union of a set of natural language reasons mapped from paths followed by a subset of the constituent decision tree models that participated in a final recommendation by the ensemble of decision trees for the data point;
• and
• for each of the data points for which recommendations are being generated, displaying the set of natural language reasons as the reasons for the recommendation by the ensemble machine learning model;
• and
• an executable portion that interacts with a user via the dialog system and engaging in a dialog with the user in relation to the questions about the data and the recommendation.