Nuance Communications, Inc.

Brian William Delaney

Girija Yegnanarayanan

Ravi Kumar Kondadadi

Radu Florian

Martin Franz

John Ferdinand Pitrelli

Salim E Roukos

Imed Zitouni

John E Ortega, Jr

Neal Snider

Neil D Barrett

Weiyi Sun

Scott McCarley

John Ortega

Oscar Ferrandez-Escamez

Techniques for coding a medical report include identifying an acronym or abbreviation in the medical report, and a plurality of phrases not explicitly included in the medical report that are possible expanded forms of the acronym or abbreviation in the medical report. From the plurality of phrases, a most likely expanded form of the acronym or abbreviation may be selected by applying to the medical report a statistical acronym/abbreviation expansion model trained on a corpus of medical reports. By applying to the medical report with the expanded acronym or abbreviation one or more statistical fact extraction models, a clinical fact may be extracted from the medical report based at least in part on the most likely expanded form of the acronym or abbreviation in the medical report, and a corresponding medical taxonomy code may be assigned to the extracted clinical fact from the medical report.

Medical report coding with acronym/abbreviation disambiguation

A method, computer program product, and computing system for processing content concerning a plurality of patients using a CAC system to define one or more billing codes concerning a social habit status of one or more patients of the plurality of patients. The one or more billing codes concerning the social habit status of the one or more patients are provided to a user for review. Feedback is received from the user concerning the accuracy of the one or more billing codes. The feedback concerning the one or more billing codes is automatically processed to define one or more confidence scores. The CAC system is trained based, at least in part, upon the one or more confidence scores.

Automated analysis system and method

According to some aspects, a system for automatically processing text comprising information regarding a patient encounter to assign medical codes to the text is provided. The system comprises at least one storage medium storing processor-executable instructions, and at least one processor configured to execute the processor-executable instructions to perform analyzing the text to extract a plurality of facts from the text, identifying at least one of the plurality of facts to be excluded from consideration when assigning medical codes to the text, and evaluating each of the plurality of facts, except for the identified at least one fact, to assign one or more medical codes to the text.

Computer assisted coding systems and methods

Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Methods and apparatus for extracting facts from a medical text

Techniques for entity detection include matching a token from at least a portion of a text string with a matching concept in an ontology, wherein the at least a portion of the text string has been labeled as corresponding to a particular entity type. A first concept may be identified as being hierarchically related to the matching concept within the ontology, and a second concept may be identified as being hierarchically related to the first concept within the ontology. Based at least in part on the labeling of the at least a portion of the text string as corresponding to the particular entity type, a statistical model may be trained to associate the first concept with a first probability of corresponding to the particular entity type and the second concept with a second probability of corresponding to the particular entity type.

Methods and apparatus for entity detection

Techniques for entity detection include matching a token from at least a portion of a text string with a matching concept in an ontology. A first concept may be identified as being hierarchically related to the matching concept within the ontology, and a second concept may be identified as being hierarchically related to the first concept within the ontology. The first and second concepts may be included in a set of features of the token. Based at least in part on the set of features of the token, a measure related to a likelihood that the at least a portion of the text string corresponds to a particular entity type may be determined.

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Bolton, MA, United States of America

Brian William Delaney