21. The at least one non-transitory computer-readable storage medium of claim 20 , wherein: updating the set of rules and/or the one or more statistical models comprises establishing one or more rules correlating the first set of features of the first portion of the text with the at least one user-specified abstract semantic concept, and extracting using the set of rules and/or the one or more statistical models following the updating comprises applying at least one of the one or more rules to the second portion of the text.
20. At least one non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed, perform a method of analyzing a text documenting a patient encounter, the method comprising: extracting from a first portion of the text, using a set of one or more rules and/or one or more statistical models implemented via at least one processor, a first set of one or more medical facts representing one or more abstract semantic concepts; receiving information identifying a change made by a user to the first set of medical facts extracted from the first portion of the text, wherein the information identifying the change identifies at least one user-specified abstract semantic concept; updating the set of rules and/or the one or more statistical models based on the at least one user-specified abstract semantic concept and the first portion of the text, wherein the updating comprises: identifying features included in the first portion of the text, the features including one or more tokens and/or one or more N-grams appearing in the first portion of the text; identifying, from among the features identified in the first portion of the text, a first set of features to associate with the at least one user-specified abstract semantic concept, the first set of features including one or more features, the one or more features of the first set including one or more tokens and/or one or more N-grams; and updating the set of rules and/or the one or more statistical models to associate the first set of features with the at least one user-specified abstract semantic concept; and extracting, using the set of rules and/or the one or more statistical models following the updating and using the at least one processor, a second set of one or more medical facts from a second portion of the text, wherein extracting the second set comprises, in response to identifying in the second portion of the text a second set of features corresponding to the first set of features, extracting from the second portion of the text one or more facts corresponding to the at least one user-specified abstract semantic concept, the second set of features including one or more features.