Abstract: A network node includes at least one processor, at least one memory, and at least one network interface. The network node is configured to be within a plurality of network nodes communicatively coupled in a peer-to-peer network of network nodes implementing a distributed ledger. The network node is configured to be communicatively coupled to at least one remotely located computing device through the at least one network interface. The at least one processor is configured to determine a split ratio for a new split of security tokens held in at least one address of the distributed ledger. The at least one processor is also configured to store an indication of the split ratio without updating token balances for all of the at least one address in a list of addresses holding at least one of the security tokens.

Abstract: A network node in a peer-to-peer network of nodes includes at least one processor, at least one memory, and at least one network interface. The at least one processor is configured to receive a request to transfer a security token and execute a plurality of compliance rules associated with the security token. At least one of the compliance rules is implemented using at least one smart contract. The at least one smart contract references a global registry. The at least one processor is also configured to transfer the security token based on the execution of the compliance rules. The global registry includes personally identifiable information (PII) hash(es) for each of at least one investor associated with the transfer of the security token. The hashes are committed to the distributed ledger and updated when PII of a respective one of the at least one investor changes.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, and more specifically, to computer network architectures in the context of program rules, using combinations of defined patient clinical episode metrics and other clinical metrics, thus enabling superior performance of computer hardware. Aspects of embodiments herein are specific to patient clinical episode definitions, and are applied to the specific outcomes of highest concern to each episode type. Furthermore, aspects of embodiments herein produce more accurate and reliable predictions of possible patient outcomes and metrics.

Abstract: Computer network architectures for machine learning, and more specifically, computer network architectures for the automated completion of healthcare claims. Embodiments of the present invention provide computer network architectures for the automated completion of estimated final cost data for claims for healthcare clinical episodes using incomplete data for healthcare insurance claims and costs, known to date. Embodiments may use an automatic claims completion web application, with other computer network architecture components. Embodiments may include a combination of third-party databases to generate estimated final claims for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, social-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and active updates of outcomes. Embodiments of computer network architecture automatically update forecasts of outcomes of patient episodes and annual costs for each patient of interest after hospital discharge. Embodiments may generate such updated forecasts either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: A metering system for a fuel atomizer includes a housing having a fuel inlet and an oxidizer inlet arranged coaxially, and an oxidizer metering device having a plurality of oxidizer channels, an oxidizer flow controller, and a fuel metering device. The oxidizer channels are spaced apart circumferentially in the housing and are arranged angled in at least one of a radially inward direction and a tangential direction to create a swirl of oxidizer flow in a mixing chamber of the fuel atomizer. The oxidizer flow controller is configured to control flow of oxidizer from the oxidizer inlet to the plurality of oxidizer channels. The fuel metering device is configured to control fuel flow from the fuel inlet to the mixing chamber.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and risk adjusted performance ranking of healthcare providers. Embodiments of computer network architecture automatically make risk adjusted performance rankings of healthcare service providers and generate and transmit reports of the rankings. Embodiments may generate such rankings either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Computer network architectures for machine learning, and more specifically, computer network architectures for the automated completion of healthcare claims. Embodiments of the present invention provide computer network architectures for the automated completion of estimated final cost data for claims for healthcare clinical episodes using incomplete data for healthcare insurance claims and costs, known to date. Embodiments may use an automatic claims completion web application, with other computer network architecture components. Embodiments may include a combination of third-party databases to generate estimated final claims for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, social-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and active updates of outcomes. Embodiments of computer network architecture automatically update forecasts of outcomes of patient episodes and annual costs for each patient of interest after hospital discharge. Embodiments may generate such updated forecasts either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments relate generally to computer network architectures for machine learning, and more specifically, to computer network architectures in the context of program rules, using combinations of defined patient clinical episode metrics and other clinical metrics, thus enabling superior performance of computer hardware. Aspects of embodiments herein are specific to patient clinical episode definitions, and are applied to the specific outcomes of highest concern to each episode type. Furthermore, aspects of embodiments herein produce more accurate and reliable predictions of possible patient outcomes and metrics.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and the automatic development of patient care groupings of patient data. Embodiments of computer network architecture automatically generate care grouping, and organize and analyse patient care data accordingly, and generate and transmit reports of the care grouping definitions, data, and analysis. Embodiments may generate care groupings either occasionally on demand, or periodically, or as triggered by events such as an update of available data. Embodiments may include a combination system databases with data provided by system users, and third-party databases to generate the patient care groupings, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and the automatic development of patient care groupings of patient data. Embodiments of computer network architecture automatically generate care grouping, and organize and analyse patient care data accordingly, and generate and transmit reports of the care grouping definitions, data, and analysis. Embodiments may generate care groupings either occasionally on demand, or periodically, or as triggered by events such as an update of available data. Embodiments may include a combination system databases with data provided by system users, and third-party databases to generate the patient care groupings, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and risk adjusted performance ranking of healthcare providers. Embodiments of computer network architecture automatically make risk adjusted performance rankings of healthcare service providers and generate and transmit reports of the rankings. Embodiments may generate such rankings either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: A metering system for a fluid atomizer includes a housing, first and second metering members, and at least one solenoid. The housing includes a mixing chamber. The first metering member is operable to control flow of a first fluid to the mixing chamber. The second metering member is arranged coaxial with the first metering member and operable to control flow of a second fluid to the mixing chamber. The at least one solenoid is configured to operate at least one of the first and second metering members.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and automated insight generation. Embodiments of computer network architecture automatically identify, measure, and generate insight reports of underperformance and over performance in healthcare practices. Embodiments may generate the insight reports of performance either occasionally on demand, or periodically, or as triggered by events such as an update of available data. Embodiments may include a combination of system databases with data provided by system users, and third-party databases to generate the insight reports, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and automated improvement and regularization of forecasting models, providing rapid improvement of the models. Embodiments may generate such rapid improvement of the models either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may indicate, after the improvement of the models, that various web applications using the models may be rerun to seek improved results for the web applications. Embodiments may include a combination of third-party databases to drive the forecasting models, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: The present disclosure is related generally to computer network architectures for machine learning, and more specifically, to computer network architectures for the automated production and distribution of custom healthcare performance benchmarks for specific patient cohorts. Embodiments allow specification and automated production of benchmarks using any of many dozens of patient, disease process, facility, and physical location attributes. Embodiments may use an analytic module web application and a benchmark service module web application, with other architecture components. Embodiments may include a combination of third-party databases to generate benchmarks and to drive the forecasting models, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: A metering system for a fluid atomizer includes a housing, first and second metering members, and at least one solenoid. The housing includes a mixing chamber. The first metering member is operable to control flow of a first fluid to the mixing chamber. The second metering member is arranged coaxial with the first metering member and operable to control flow of a second fluid to the mixing chamber. The at least one solenoid is configured to operate at least one of the first and second metering members.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and automated improvement and regularization of forecasting models, providing rapid improvement of the models. Embodiments may generate such rapid improvement of the models either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may indicate, after the improvement of the models, that various web applications using the models may be rerun to seek improved results for the web applications. Embodiments may include a combination of third-party databases to drive the forecasting models, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: A metering system for a fluid atomizer includes a housing, first and second metering members, and at least one solenoid. The housing includes a mixing chamber. The first metering member is operable to control flow of a first fluid to the mixing chamber. The second metering member is arranged coaxial with the first metering member and operable to control flow of a second fluid to the mixing chamber. The at least one solenoid is configured to operate at least one of the first and second metering members.