1. A method comprising: extracting, from a text documenting a clinician's encounter with a single patient, a set of one or more clinical facts representing one or more abstract semantic concepts, wherein the extracting comprises analyzing the text, via a natural language understanding engine, to identify a set of one or more features of at least a portion of the text, and correlating the set of features to the one or more abstract semantic concepts; wherein the one or more abstract semantic concepts comprise a first diagnosis that the clinician, in the text, indicated that the patient exhibited; wherein the first diagnosis is a generic diagnosis representing a class of a plurality of more specific subdiagnoses of the first diagnosis; wherein the method further comprises: analyzing a history record comprising data indicative of the patient's history to determine an additional fact without needing to request input of the additional fact; analyzing the set of one or more clinical facts and the additional fact, using at least one processor, to generate one or more hypotheses for a second diagnosis, exhibited by the patient and not documented in the text, the second diagnosis being a particular one of the plurality of more specific subdiagnoses of the first diagnosis that the clinician indicated that the patient exhibited; and presenting, to a user, the generated at least one of the one or more hypotheses.