14. At least one non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed by at least one processor, perform a method comprising: accessing a text documenting a patient encounter; analyzing the text to extract a first set of one or more medical facts from a first portion of the text; the extracting comprising processing the text to identify a set of one or more features of the first portion of the text, statistically correlating the set of features of the first portion of the text to a set of one or more abstract semantic concepts, and generating computer-readable data that expresses the set of abstract semantic concepts in human-understandable form as the first set of medical facts extracted from the first portion of the text; receiving, from a user, a correction to the first set of medical facts extracted from the first portion of the text, wherein the correction identifies at least one user-specified abstract semantic concept; processing the user's correction to create updated programming for the at least one processor based on an association between the identified at least one user-specified abstract semantic concept and the first portion of the text; and applying the updated programming in extracting a second set of one or more medical facts from a second portion of the text, wherein applying the updated programming comprises extracting one or more facts similar to the identified at least one user-specified abstract semantic concept from the second portion of the text.
14. At least one non-transitory computer-readable storage medium encoded with computer-executable instructions that, when executed by at least one processor, perform a method comprising: accessing a text documenting a patient encounter; analyzing the text to extract a first set of one or more medical facts from a first portion of the text; the extracting comprising processing the text to identify a set of one or more features of the first portion of the text, statistically correlating the set of features of the first portion of the text to a set of one or more abstract semantic concepts, and generating computer-readable data that expresses the set of abstract semantic concepts in human-understandable form as the first set of medical facts extracted from the first portion of the text; receiving, from a user, a correction to the first set of medical facts extracted from the first portion of the text, wherein the correction identifies at least one user-specified abstract semantic concept; processing the user's correction to create updated programming for the at least one processor based on an association between the identified at least one user-specified abstract semantic concept and the first portion of the text; and applying the updated programming in extracting a second set of one or more medical facts from a second portion of the text, wherein applying the updated programming comprises extracting one or more facts similar to the identified at least one user-specified abstract semantic concept from the second portion of the text. 15. The at least one non-transitory computer-readable storage medium of claim 14 , wherein the identified at least one user-specified abstract semantic concept corresponds to a particular fact type, and wherein extracting the second set of facts comprises labeling one or more tokens in the second portion of the text as corresponding to the particular fact type.