Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for a hybrid question-answering (QA) application are provided. The hybrid QA application includes both a retrieval QA application and a deep QA application, and uses feedback from the retrieval QA application to improve the deep QA application.

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: Methods, apparatus, systems, computing devices, computing entities, and/or the like for a hybrid question-answering (QA) application are provided. The hybrid QA application includes both a retrieval QA application and a deep QA application, as well as a question classification model that determines whether the retrieval QA application or the deep QA application can generate a better answer to an input question.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for a hybrid question-answering (QA) application are provided. The hybrid QA application includes both a retrieval QA application and a deep QA application, as well as a question classification model that determines whether the retrieval QA application or the deep QA application can generate a better answer to an input question.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for a hybrid question-answering (QA) application are provided. The hybrid QA application includes both a retrieval QA application and a deep QA application, and uses feedback from the retrieval QA application to improve the deep QA application.

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: A method includes receiving a corpus comprising a set of pre-segmented texts. The method further includes creating a plurality of modified pre-segmented texts for the set of pre-segmented texts by extracting a set of semantic terms for each pre-segmented text within the set of pre-segmented texts and applying at least one domain tag for each pre-segmented text within the set of pre-segmented texts. The method further includes clustering the plurality of modified pre-segmented texts into one or more conceptual units, wherein each of the one or more conceptual units is associated with one or more templates, wherein each of the one or more templates corresponds to one of the plurality of modified pre-segmented texts.

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 method includes receiving a corpus comprising a set of pre-segmented texts. The method further includes creating a plurality of modified pre-segmented texts for the set of pre-segmented texts by extracting a set of semantic terms for each pre-segmented text within the set of pre-segmented texts and applying at least one domain tag for each pre-segmented text within the set of pre-segmented texts. The method further includes clustering the plurality of modified pre-segmented texts into one or more conceptual units, wherein each of the one or more conceptual units is associated with one or more templates, wherein each of the one or more templates corresponds to one of the plurality of modified pre-segmented texts.

Abstract: A method includes receiving a corpus comprising a set of pre-segmented texts. The method further includes creating a plurality of modified pre-segmented texts for the set of pre-segmented texts by extracting a set of semantic terms for each pre-segmented text within the set of pre-segmented texts and applying at least one domain tag for each pre-segmented text within the set of pre-segmented texts. The method further includes clustering the plurality of modified pre-segmented texts into one or more conceptual units, wherein each of the one or more conceptual units is associated with one or more templates, wherein each of the one or more templates corresponds to one of the plurality of modified pre-segmented texts.

Abstract: A method includes receiving a corpus comprising a set of pre-segmented texts. The method further includes creating a plurality of modified pre-segmented texts for the set of pre-segmented texts by extracting a set of semantic terms for each pre-segmented text within the set of pre-segmented texts and applying at least one domain tag for each pre-segmented text within the set of pre-segmented texts. The method further includes clustering the plurality of modified pre-segmented texts into one or more conceptual units, wherein each of the one or more conceptual units is associated with one or more templates, wherein each of the one or more templates corresponds to one of the plurality of modified pre-segmented texts.

Abstract: Some known medical terms may function as non-medical terms depending on their particular context. Accordingly, the present inventors devised systems, methods, and software that facilitate determining whether a term that is found in a medical corpus is likely to be a medical term when found in another corpus. An exemplary embodiment receives a term and computes an ambiguity score based on language models for a medical and a non-medical corpus.

Abstract: A method includes receiving a corpus comprising a set of pre-segmented texts. The method further includes creating a plurality of modified pre-segmented texts for the set of pre-segmented texts by extracting a set of semantic terms for each pre-segmented text within the set of pre-segmented texts and applying at least one domain tag for each pre-segmented text within the set of pre-segmented texts. The method further includes clustering the plurality of modified pre-segmented texts into one or more conceptual units, wherein each of the one or more conceptual units is associated with one or more templates, wherein each of the one or more templates corresponds to one of the plurality of modified pre-segmented texts.

Abstract: Some known medical terms may function as non-medical terms depending on their particular context. Accordingly, the present inventors devised systems, methods, and software that facilitate determining whether a term that is found in a medical corpus is likely to be a medical term when found in another corpus. An exemplary embodiment receives a term and computes an ambiguity score based on language models for a medical and a non-medical corpus.

Abstract: A document search and retrieval system and method stores documents in groups based on content. The documents are self-organized into a hierarchy of conceptual clusters, and branches of the hierarchy are stored separately in distinct physical stores, each having an index. In response to a query, the system finds the concepts (clusters) that best match the search criteria and returns the documents from those content categories. The indexing, clustering, and searching are performed using document themes and/or summaries. Themes are automatically developed by stemming and scoring phrases from the sentences in each document, and clustering the sentences containing the highest-scoring stems. A set of phrases (themes) is taken from each cluster. Document summaries are taken from text segments for each cluster of sentences within a document, then strung together to create a summary.