Abstract: Techniques for dynamic visualization of data are provided. A plurality of therapies is received, where each of the plurality of therapies is associated with a respective plurality of guidelines. A guideline tree is generated based on the plurality of therapies, where each leaf node in the guideline tree represents a respective therapy, and where each edge in the guideline tree represents a respective guideline. A visual depiction of the guideline tree is generated. Further, a first plurality of attributes associated with a first patient is received, and a first modified visual depiction of the guideline tree is generated based on the first plurality of attributes.

Abstract: Method and apparatus for performing feature engineering using negative inferences are provided. One example method generally includes identifying a plurality of concepts and analyzing a corpus of documents to determine a first co-occurrence rate for a first concept and a second concept in the plurality of concepts. The method further includes analyzing the corpus of documents to determine a second co-occurrence rate for the second concept and at least a third concept of a set of concepts related to the first concept and determining an inverse relationship between the second concept and the third concept. The method further includes generating test data for training a machine learning model including a negative inference between the second concept and the third concept and training the machine learning model using the test data.

Abstract: A computing system receives a plurality of medical perspectives for each medical treatment option of a plurality of medical treatment options. A machine learning model assigns a weight to each medical perspective for each medical treatment option by determining how often a care provider has agreed with each perspective within the plurality of medical perspectives. The computing system receives a medical treatment option ranking for each of the medical perspectives. The computing system generates a score for each medical perspective as applied to each medical treatment option based on a combination of the weight and the ranking for each medical perspective. The computing system aggregates the scores across the plurality of medical perspectives into an overall score for each medical treatment option.

Abstract: Embodiments provide for reduced user input requests by identifying predefined diagnosis paradigms; creating a synthetic diagnosis paradigm via a machine learning process based on prior selections of action plans recommended by the predefined diagnosis paradigms and values entered therefor, wherein the synthetic diagnosis paradigm identifies the action plans to treat the plurality of conditions based on a subset of the attribute inputs used by the predefined diagnosis paradigms; generating a graphical user interface (GUI) to prompt input for values for the subset of attribute inputs; in response to receiving the values for the subset of attribute inputs, identifying at least one condition according to the predefined and synthetic diagnosis paradigms; and displaying the action plans in the GUI in association with the synthetic and predefined diagnosis paradigms according to evaluations of the action plans based on the respective logical structures.

Abstract: Mechanisms are provided which configure a medical treatment recommendation system to implement a medical condition independent treatment recommendation model. The medical condition independent treatment recommendation model operates on medical condition independent scoring features that are independent of any specific medical condition. The mechanisms configure the medical treatment recommendation system to receive a medical condition cartridge providing at least one medical condition specific evaluation feature that is specific to a medical condition, and process patient information based on a combination of an application of the at least one medical condition specific evaluation feature, and an execution of the medical condition independent treatment recommendation model of the medical treatment recommendation system, to generate a treatment recommendation for a patient medical condition associated with the patient. The mechanisms then output the treatment recommendation for the patient.

Abstract: Managing answers in a question-answering environment is disclosed. Managing answers in the question-answering environment can include sorting, based on a set of answer categories for a subject matter, a first set of answers into a first answer category and a second set of answers into a second answer category. Managing answers in the question-answering environment can include determining, using the subject matter, a first category sequence including the first answer category and the second answer category, and establishing, based on the first category sequence, a first answer sequence established from a portion of the first set of answers from the first answer category and a portion of the second set of answers from the second answer category.

Abstract: Managing answers in a question-answering environment is disclosed. Managing answers in the question-answering environment can include sorting, based on a set of answer categories for a subject matter, a first set of answers into a first answer category and a second set of answers into a second answer category. Managing answers in the question-answering environment can include determining, using the subject matter, a first category sequence including the first answer category and the second answer category, and establishing, based on the first category sequence, a first answer sequence established from a portion of the first set of answers from the first answer category and a portion of the second set of answers from the second answer category.

Abstract: A computing system receives a plurality of medical perspectives for each medical treatment option of a plurality of medical treatment options. A machine learning model assigns a weight to each medical perspective for each medical treatment option by determining how often a care provider has agreed with each perspective within the plurality of medical perspectives. The computing system receives a medical treatment option ranking for each of the medical perspectives. The computing system generates a score for each medical perspective as applied to each medical treatment option based on a combination of the weight and the ranking for each medical perspective. The computing system aggregates the scores across the plurality of medical perspectives into an overall score for each medical treatment option.

Abstract: Embodiments provide for the dynamic adjustment of graphical user interfaces (GUIs) in response to learned user preferences via generating a plurality of scores for a plurality of action plans based on logical structures defined by a plurality of diagnosis paradigms that identify conditions addressable by individual action; determining a plurality of weights associated with the plurality of diagnosis paradigms, wherein a given weight of the plurality of weights is based on a historic frequency of selection of individual action plans from a GUI associated with a particular diagnosis paradigm; determining a concordance measure for each action plan relative to each other action plan based on a machine learning clustering of the action plans using the plurality of weights and the plurality of scores; and generating the GUI to present the plurality of diagnosis paradigms and the plurality of action plans based on a respective concordance measure for each action plan.

Abstract: Method and apparatus for performing feature engineering using negative inferences are provided. One example method generally includes identifying a plurality of concepts and analyzing a corpus of documents to determine a first co-occurrence rate for a first concept and a second concept in the plurality of concepts. The method further includes analyzing the corpus of documents to determine a second co-occurrence rate for the second concept and at least a third concept of a set of concepts related to the first concept and determining an inverse relationship between the second concept and the third concept. The method further includes generating test data for training a machine learning model including a negative inference between the second concept and the third concept and training the machine learning model using the test data.

Abstract: Disclosed aspects relate to generating rationales for treatment options. A set of preference scores that indicates a first preference score for a first treatment option of a set of treatment options may be received. A rank-order that indicates a first ranking for the first treatment option may be received. The set of preference scores may be analyzed with respect to the rank-order to determine a relationship between the first preference score and the first ranking for the first treatment option. Based on the relationship between the first preference score and the first ranking, a set of rationale data for the first treatment option may be generated with respect to the first rank. Based on a user profile for a user, the set of rationale data may be configured for the user. The set of rationale data which is configured for the user may be provided.

Abstract: Embodiments provide for reduced user input requests by identifying predefined diagnosis paradigms; creating a synthetic diagnosis paradigm via a machine learning process based on prior selections of action plans recommended by the predefined diagnosis paradigms and values entered therefor, wherein the synthetic diagnosis paradigm identifies the action plans to treat the plurality of conditions based on a subset of the attribute inputs used by the predefined diagnosis paradigms; generating a graphical user interface (GUI) to prompt input for values for the subset of attribute inputs; in response to receiving the values for the subset of attribute inputs, identifying at least one condition according to the predefined and synthetic diagnosis paradigms; and displaying the action plans in the GUI in association with the synthetic and predefined diagnosis paradigms according to evaluations of the action plans based on the respective logical structures.

Abstract: Techniques for dynamic visualization of data are provided. A plurality of therapies is received, where each of the plurality of therapies is associated with a respective plurality of guidelines. A guideline tree is generated based on the plurality of therapies, where each leaf node in the guideline tree represents a respective therapy, and where each edge in the guideline tree represents a respective guideline. A visual depiction of the guideline tree is generated. Further, a first plurality of attributes associated with a first patient is received, and a first modified visual depiction of the guideline tree is generated based on the first plurality of attributes.

Abstract: Mechanisms are provided for extending a medical condition base cartridge (MCBC) based on user modifications of the MCBC. The mechanisms generate a MCBC for a medical condition at least by performing cognitive processing of natural language content in a corpus to extract patient attributes and treatment information associated with the medical condition. Information stored in the MCBC is presented to a user via at least one interface and user input is received, where the user input indicates at least one of a modification of, addition to, or deletion of, a portion of the information presented to generate an extension to the MCBC. The extension to the MCBC is stored as an expert medical condition cartridge (EMCC) in association with the MCBC in a repository. The EMCC is input to a medical cognitive system to perform a medical cognitive operation on patient information based on the EMCC.

Abstract: Methods to, responsive to receiving a case by a deep question answering (deep QA) system, identify, in a corpus of information, a first variable for which a value was not specified in the case, compute an importance score for the first variable based on a concept in the corpus, wherein the concept is associated with the first variable, and upon determining that the importance score exceeds an importance threshold, determine that specifying a value for the first variable increases a confidence score of a response returned by the deep QA system beyond a confidence threshold.

Abstract: Systems and computer program products to, responsive to receiving a case by a deep question answering (deep QA) system, identify, in a corpus of information, a first variable for which a value was not specified in the case, compute an importance score for the first variable based on a concept in the corpus, wherein the concept is associated with the first variable, and upon determining that the importance score exceeds an importance threshold, determine that specifying a value for the first variable increases a confidence score of a response returned by the deep QA system beyond a confidence threshold.

Abstract: Systems and computer program products to perform an operation comprising: identifying a first attribute of a source code in a deep question answering system, computing an influence score for the first attribute based on a rule in the source code used to compute a confidence score for each of a plurality of candidate answers generated by the deep question answering system, computing an importance score for the first attribute based at least in part on the computed influence score, and upon determining that the importance score exceeds a predefined threshold, storing an indication that the first attribute is an important attribute relative to other attributes specified in the source code.

Abstract: Aspects of the present disclosure are directed toward discovering and generating answer sequences. Aspects are directed toward parsing, by a natural language processing technique configured to analyze syntactic and semantic content, a corpus of data for a subject matter. Aspects are also directed toward detecting a first set of answers including a first answer corresponding to a first answer category and a second set of answers including a second answer corresponding to a second answer category. Both the first and the second answer categories may relate to the subject matter. Aspects are also directed toward identifying a first set of ordering data for the first set of answers and the second set of answers. Aspects are also directed toward determining a first answer sequence corresponding to an order of the first set of answers and the second set of answers.

Abstract: Disclosed aspects relate to generating rationales for treatment options. A set of preference scores that indicates a first preference score for a first treatment option of a set of treatment options may be received. A rank-order that indicates a first ranking for the first treatment option may be received. The set of preference scores may be analyzed with respect to the rank-order to determine a relationship between the first preference score and the first ranking for the first treatment option. Based on the relationship between the first preference score and the first ranking, a set of rationale data for the first treatment option may be generated with respect to the first rank. Based on a user profile for a user, the set of rationale data may be configured for the user. The set of rationale data which is configured for the user may be provided.

Abstract: Methods to perform an operation comprising: identifying a first attribute of a source code in a deep question answering system, computing an influence score for the first attribute based on a rule in the source code used to compute a confidence score for each of a plurality of candidate answers generated by the deep question answering system, computing an importance score for the first attribute based at least in part on the computed influence score, and upon determining that the importance score exceeds a predefined threshold, storing an indication that the first attribute is an important attribute relative to other attributes specified in the source code.