Abstract: Techniques for presenting alternative hypotheses for medical facts may include identifying, using at least one statistical fact extraction model, a plurality of alternative hypotheses for a medical fact to be extracted from a portion of text documenting a patient encounter. At least two of the alternative hypotheses may be selected, and the selected hypotheses may be presented to a user documenting the patient encounter.

Abstract: An original text that is a representation of a narration of a patient encounter provided by a clinician may be received and re-formatted to produce a formatted text. One or more clinical facts may be extracted from the formatted text. A first fact of the clinical facts may be extracted from a first portion of the formatted text, and the first portion of the formatted text may be a formatted version of a first portion of the original text. A linkage may be maintained between the first fact and the first portion of the original text.

Abstract: An original text that is a representation of a narration of a patient encounter provided by a clinician may be received and re-formatted to produce a formatted text. One or more clinical facts may be extracted from the formatted text. A first fact of the clinical facts may be extracted from a first portion of the formatted text, and the first portion of the formatted text may be a formatted version of a first portion of the original text. A linkage may be maintained between the first fact and the first portion of the original text.

Abstract: Techniques for presenting alternative hypotheses for medical facts may include identifying, using at least one statistical fact extraction model, a plurality of alternative hypotheses for a medical fact to be extracted from a portion of text documenting a patient encounter. At least two of the alternative hypotheses may be selected, and the selected hypotheses may be presented to a user documenting the patient encounter.

Abstract: 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.

Abstract: 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.

Abstract: Some aspect include a system for automatically processing text comprising information regarding a patient encounter to prioritize medical billing codes derived from the text. 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 analyze the text to extract a plurality of facts from the text, assign a plurality of medical billing codes to the text based at least in part on the plurality of facts, using a model trained at least in part on feedback from a user, order the plurality of medical billing codes in a sequence beginning with a primary medical billing code corresponding to a primary diagnosis associated with the text, and present the ordered sequence of medical billing codes to the user for review.

Abstract: Techniques for presenting alternative hypotheses for medical facts may include identifying, using at least one statistical fact extraction model, a plurality of alternative hypotheses for a medical fact to be extracted from a portion of text documenting a patient encounter. At least two of the alternative hypotheses may be selected, and the selected hypotheses may be presented to a user documenting the patient encounter.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques for presenting alternative hypotheses for medical facts may include identifying, using at least one statistical fact extraction model, a plurality of alternative hypotheses for a medical fact to be extracted from a portion of text documenting a patient encounter. At least two of the alternative hypotheses may be selected, and the selected hypotheses may be presented to a user documenting the patient encounter.

Abstract: An original text that is a representation of a narration of a patient encounter provided by a clinician may be received and re-formatted to produce a formatted text. One or more clinical facts may be extracted from the formatted text. A first fact of the clinical facts may be extracted from a first portion of the formatted text, and the first portion of the formatted text may be a formatted version of a first portion of the original text. A linkage may be maintained between the first fact and the first portion of the original text.

Abstract: Some aspect include a system for automatically processing text comprising information regarding a patient encounter to prioritize medical billing codes derived from the text. 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 analyze the text to extract a plurality of facts from the text, assign a plurality of medical billing codes to the text based at least in part on the plurality of facts, using a model trained at least in part on feedback from a user, order the plurality of medical billing codes in a sequence beginning with a primary medical billing code corresponding to a primary diagnosis associated with the text, and present the ordered sequence of medical billing codes to the user for review.

Abstract: Techniques for applying user corrections to medical fact extraction may include extracting a first set of one or more medical facts from a first portion of text documenting a patient encounter. A correction to the first set of medical facts may be received from a user. The correction may identify a fact that should be associated with the first portion of the text. A second set of one or more medical facts may be extracted from a second portion of the text based at least in part on the user's correction to the first set of medical facts. Extracting the second set of facts may include extracting one or more facts similar to the identified fact from the second portion of the text.

Abstract: An original text that is a representation of a narration of a patient encounter provided by a clinician may be received and re-formatted to produce a formatted text. One or more clinical facts may be extracted from the formatted text. A first fact of the clinical facts may be extracted from a first portion of the formatted text, and the first portion of the formatted text may be a formatted version of a first portion of the original text. A linkage may be maintained between the first fact and the first portion of the original text.

Abstract: Techniques for presenting alternative hypotheses for medical facts may include identifying, using at least one statistical fact extraction model, a plurality of alternative hypotheses for a medical fact to be extracted from a portion of text documenting a patient encounter. At least two of the alternative hypotheses may be selected, and the selected hypotheses may be presented to a user documenting the patient encounter.

Abstract: 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.

Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.