Abstract: The present disclosure directs to systems and methods for natural pseudonymization of text. A natural pseudonym has at least one information attribute same as a piece of sensitive text information. The systems and methods can identify sensitive text information, select a natural pseudonym, and modify a data stream of text data by replacing the piece of sensitive text information with the natural pseudonym.

Abstract: This disclosure describes systems, devices, and techniques for identifying sections of medical documents that are suitable for automated medical coding. In one example, a computer-implemented method includes receiving, by one or more processors, the medical document, wherein the medical document comprises a plurality of sections. The method also may include determining, by the one or more processors and via application of a classification model to each section of the plurality of sections, codability indicia for each section of the plurality of sections, wherein the codability indicia represents whether the respective section is suitable for automated medical coding. The method may include outputting, by the one or more processors, the respective codability indicia for each section of the plurality of sections.

Abstract: This disclosure describes systems, devices, and techniques for identifying sections of medical documents that are suitable for automated medical coding. In one example, a computer-implemented method includes receiving, by one or more processors, the medical document, wherein the medical document comprises a plurality of sections. The method also may include determining, by the one or more processors and via application of a classification model to each section of the plurality of sections, codability indicia for each section of the plurality of sections, wherein the codability indicia represents whether the respective section is suitable for automated medical coding. The method may include outputting, by the one or more processors, the respective codability indicia for each section of the plurality of sections.

Abstract: Apparatus and methods for determining whether a word/sense pair is proper for a context. Wide contexts (100 words) are employed for both training and testing, and testing is done by adding the weights of vocabulary words from the context. The weights are determined by Bayesian techniques which interpolate between the probability of occurrence of a vocabulary word in a conditional sample of the training text and the probability of its occurrence in the entire training text. A further improvement in testing takes advantage of the fact that a word is generally used in only a single sense in a single discourse. Also disclosed are automated training techniques including training on bilingual bodies of text and training using categories from Roget's Thesaurus.