Abstract: A plurality of attributes are extracted from a plurality of electronic health records, where each electronic health record is associated with a patient in a plurality of patients. Additionally, a training data set and a scoring data set are generated based on the plurality of attributes, and a patient similarity model is trained based on the training data set. A precision cohort is identified, where the precision cohort includes patients in the plurality of patients from the scoring data set that are similar to a first patient based on an electronic health record of the first patient and the similarity model. At least one result statistic for each of a plurality of treatments given to patients in the precision cohort is determined, and a first treatment of the plurality of treatments is selected for the first patient based at least in part on the determined result statistics.

Abstract: A method, apparatus, system, and computer program product for training a global machine learning model. A hierarchical structure for nodes in which the global machine learning model is located at a primary node in the hierarchical structure is identified. Authorized nodes in which local data is authorized for use in training in the authorized nodes for a local training of local machine learning models are determined. The machine learning models in the authorized nodes are trained using the local data in the authorized nodes to generate local model updates to weights in the local machine learning models. The local model updates to the weights are propagated upward in the hierarchical structure to the global machine learning model, wherein a node receiving local model updates to the weights from nodes from a lower level aggregates the weights in the local model updates received from the nodes in the lower level.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate anonymizing a network based on factors including network attributes, node attributes, and edge attributes describing connections between nodes are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an anonymizing component that can anonymize network information of the network based on a network attribute for a network and a node attribute of a first node of the network, resulting in an anonymized network.

Abstract: Provided is a method, computer program product, and system for recommending a patient obtain a second medical opinion (SMO) from a second healthcare provider. A processor may collect a first set of attributes associated with a first healthcare provider, the first healthcare provider having identified a first medical opinion for treating a medical condition of a patient. The processor may identify a plurality of healthcare providers that have treated a similar medical condition for one or more other patients. The processor may compare the first set of attributes to a plurality of attributes related to the plurality of healthcare providers. The processor may identify a second healthcare provider based on analyzing similarities between a second set of attributes related to the second healthcare provider and the first set of attributes. Once identified, the processor may provide a recommendation to the patient to obtain a SMO from the second healthcare provider.

Abstract: A method, apparatus, system, and computer program product for training a global machine learning model. A hierarchical structure for nodes in which the global machine learning model is located at a primary node in the hierarchical structure is identified. Authorized nodes in which local data is authorized for use in training in the authorized nodes for a local training of local machine learning models are determined. The machine learning models in the authorized nodes are trained using the local data in the authorized nodes to generate local model updates to weights in the local machine learning models. The local model updates to the weights are propagated upward in the hierarchical structure to the global machine learning model, wherein a node receiving local model updates to the weights from nodes from a lower level aggregates the weights in the local model updates received from the nodes in the lower level.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate anonymizing a network based on factors including network attributes, node attributes, and edge attributes describing connections between nodes are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an anonymizing component that can anonymize network information of the network based on a network attribute for a network and a node attribute of a first node of the network, resulting in an anonymized network.

Abstract: A computer-implemented method can include obtaining first website data that corresponds to content displayed on a first website. The method can further include obtaining a set of privacy policy rules that corresponds to the first website. The method can further include determining a first data-sharing relationship between the first website and a second website. The method can further include comparing the set of privacy policy rules to the first data-sharing relationship. The method can further include identifying a discrepancy between the set of privacy policy rules and the first data-sharing relationship. The method can further include generating a notification in response to identifying the discrepancy.

Abstract: A method, computer system, and a computer program product for SLA management is provided. The method may include collecting metrics from services within a composite service. The method may include determining, by a first smart contract, a first violation occurred between a first pair of services, whereby the first smart contract and the first pair of services are associated with a first private channel within a blockchain network. The method may include determining, by a second smart contract, a second SLA violation occurred between a second pair of services, whereby the second smart contract and the second pair of services are associated with a second private channel. The method may include determining that the first SLA violation and second SLA violation are related and the second SLA violation occurred before the first SLA violation. The method may include identifying a violating service within the second pair of services.