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: 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: 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: 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: A method in one embodiment includes establishing communication between a master node and several federated nodes, wherein each node is an instance of a distributed application; initializing the distributed application, wherein the initialization includes the federated nodes processing data local thereto and providing information about the local data to the master node; and after initialization, at run time, receiving data requests, wherein the master node services the data requests by routing the requests to the nodes.

Abstract: A method in one embodiment includes establishing communication between a master node and several federated nodes, wherein each node is an instance of a distributed application; initializing the distributed application, wherein the initialization includes the federated nodes processing data local thereto and providing information about the local data to the master node; and after initialization, at run time, receiving data requests, wherein the master node services the data requests by routing the requests to the nodes.