Abstract: A method, computer program product, and computing system for processing content concerning a plurality of patients using a CAC system to define one or more billing codes concerning a social habit status of one or more patients of the plurality of patients. The one or more billing codes concerning the social habit status of the one or more patients are provided to a user for review. Feedback is received from the user concerning the accuracy of the one or more billing codes. The feedback concerning the one or more billing codes is automatically processed to define one or more confidence scores. The CAC system is trained based, at least in part, upon the one or more confidence scores.

Abstract: A method, computer program product, and computing system for processing content concerning a plurality of patients using a CAC system to define one or more billing codes concerning a social habit status of one or more patients of the plurality of patients. The one or more billing codes concerning the social habit status of the one or more patients are provided to a user for review. Feedback is received from the user concerning the accuracy of the one or more billing codes. The feedback concerning the one or more billing codes is automatically processed to define one or more confidence scores. The CAC system is trained based, at least in part, upon the one or more confidence scores.

Abstract: For automated text processing, the inventors devised, among other things, an exemplary system that includes an entity tagger, an entity resolver, a text segment classifier, and a relationship extractor. The entity tagger receives an input text segment, and tags named entities with the segment as being a person, company, or place. The entity resolver accesses authority files, and associates the persons and companies named in the text segment with specific entries in the files. The text segment classifier determines whether the text segment includes a relationship event, such as job-change event or merger and acquisition event, and if an event is detected, the relationship extractor determines the event role of entities named in the segment. For example, the extractor determines for a merger and acquisition event, which named company was the acquirer and which was acquired.