1. A method of analyzing a text documenting a patient encounter, the method comprising: extracting from a first portion of the text, using a fact extraction component 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 fact extraction component 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 fact extraction component to associate the first set of features with the at least one user-specified abstract semantic concept; and extracting, using the fact extraction component 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.
1. A method of analyzing a text documenting a patient encounter, the method comprising: extracting from a first portion of the text, using a fact extraction component 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 fact extraction component 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 fact extraction component to associate the first set of features with the at least one user-specified abstract semantic concept; and extracting, using the fact extraction component 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. 7. The method of claim 1 , wherein updating the fact extraction component comprises re-training a statistical model used to extract the medical facts, the statistical model having been trained using original training data prior to extracting the first set of medical facts, the re-training comprising: creating updated training data by adding the first portion of the text, labeled with the identified at least one user-specified abstract semantic concept, to the original training data; and re-training the statistical model using the updated training data.