Abstract: A virtual assistant server extracts concept(s) from a received user query and correlates the extracted concept(s) to a plurality of nodes in a knowledge graph to identify qualifying path(s) in the knowledge graph. Further, when the two or more qualifying paths are identified, the virtual assistant server addresses one or more ambiguities in the knowledge graph by: prompting the user device to resolve a current one of the one or more ambiguities progressing from contextual nodes towards leaf nodes in the knowledge graph; receiving a response to the prompt from the user device; and repeating the prompting and the receiving until each of the one or more ambiguities is resolved and a matching path to the received query is identified. Finally, the virtual assistant server outputs content associated with a leaf node of the matched path to the user device.

Abstract: A virtual assistant server extracts concept(s) from a received user query and correlates the extracted concept(s) to a plurality of nodes in a knowledge graph to identify qualifying path(s) in the knowledge graph. Further, when the two or more qualifying paths are identified, the virtual assistant server addresses one or more ambiguities in the knowledge graph by: prompting the user device to resolve a current one of the one or more ambiguities progressing from contextual nodes towards leaf nodes in the knowledge graph; receiving a response to the prompt from the user device; and repeating the prompting and the receiving until each of the one or more ambiguities is resolved and a matching path to the received query is identified. Finally, the virtual assistant server outputs content associated with a leaf node of the matched path to the user device.

Abstract: Techniques disclosed herein provide a method of informing blood product administration to a perioperative patient. Some patients may be at risk of perioperative bleeding following a surgery, such as a cardiac surgery. As discussed herein, coagulation status of a patient is monitored perioperatively and at least one value indicative of the coagulation status may be used to determine the patient’s risk of perioperative bleeding. Should the patient’s risk of perioperative bleeding be determined to be sufficiently high, such as when the value(s) are below one or more thresholds, techniques disclosed herein may also in some embodiments aid in titration of a blood product to be administered to the perioperative patient to adjust the patient’s risk of bleeding.

Abstract: A method for detecting communities in a network comprises the steps of defining an internal scoring function (IS) for a community, wherein the internal scoring function quantifies an internal quality of the community, and defining an external scoring function (ES) for a community, wherein the external scoring function quantifies an external quality of the community. A community scoring function (IE) for a community is determined, wherein the community scoring function comprises a product of the internal scoring function and the external scoring function. A network scoring function is determined, wherein the network scoring function is based on the community scoring functions of communities in the network. The network scoring function is used by a maximization algorithm, for example a greedy maximization algorithm, for detecting a community in a network.

Abstract: A method for detecting communities in a network comprises the steps of defining an internal scoring function (IS) for a community, wherein the internal scoring function quantifies an internal quality of the community, and defining an external scoring function (ES) for a community, wherein the external scoring function quantifies an external quality of the community. A community scoring function (IE) for a community is determined, wherein the community scoring function comprises a product of the internal scoring function and the external scoring function. A network scoring function is determined, wherein the network scoring function is based on the community scoring functions of communities in the network. The network scoring function is used by a maximization algorithm, for example a greedy maximization algorithm, for detecting a community in a network.