1. A method comprising: accessing a text documenting a patient encounter; analyzing the text, using at least one processor, 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, using the at least one processor, 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.
1. A method comprising: accessing a text documenting a patient encounter; analyzing the text, using at least one processor, 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, using the at least one processor, 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. 6. The method of claim 1 , wherein creating the updated programming comprises re-training a statistical model used to extract the medical facts, wherein the statistical model was 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.