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公开(公告)号：US12079230B1
公开(公告)日：2024-09-03
申请号：US18428964
申请日：2024-01-31
发明人： Phillip H. Rogers , Jonathan B. Ward , Rashmi Poudel , Emily Barry , Melinda Sue Gomez Tellez , Prajwal Vijendra , Azriel S. Ghadooshahy , Emmet Sun
IPC分类号： G06F16/2457 , G06F16/22
CPC分类号： G06F16/24578 , G06F16/2282
摘要： Embodiments in the present disclosure relate to computer network architectures and methods for predictive analysis using lookup tables as prediction models. The predictive analysis, including the generation of the lookup tables, performed by a predictive system of the present disclosure is driven entirely by a query language, such as Structured Query Language, in various embodiments. The predictive analysis, including the generation of the lookup tables, is performed without machine learning or generative artificial intelligence, in various embodiments.
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公开(公告)号：US10910113B1
公开(公告)日：2021-02-02
申请号：US16847179
申请日：2020-04-13
发明人： Jean P. Drouin , Samuel H. Bauknight , Todd Gottula , Yale Wang , Adam F. Rogow , Justin Warner
摘要： 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.
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公开(公告)号：US11763950B1
公开(公告)日：2023-09-19
申请号：US15998771
申请日：2018-08-16
发明人： Jeffrey D. Larson , Yale Wang , Samuel H. Bauknight , Justin Warner , Todd Gottula , Jean P. Drouin
摘要： 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.
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4.发明授权公开(公告)号：US11270785B1
公开(公告)日：2022-03-08
申请号：US16697773
申请日：2019-11-27
发明人： Erik Talvola , Emmet Sun , Adam F. Rogow , Jeffrey D. Larson , Justin Warner
摘要： 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.
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公开(公告)号：US10726359B1
公开(公告)日：2020-07-28
申请号：US16533465
申请日：2019-08-06
发明人： Jean P. Drouin , Samuel H. Bauknight , Todd Gottula , Yale Wang , Adam F. Rogow , Jeffrey D. Larson , Justin Warner
摘要： 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.
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公开(公告)号：US10650928B1
公开(公告)日：2020-05-12
申请号：US15845633
申请日：2017-12-18
摘要： A computer network architecture for a pipeline of models with machine learning and artificial intelligence for healthcare outcomes is presented. A machine learning prediction module and an artificial intelligence learning model are in electronic communication with a web application, which is also in electronic communication with a user device. An expanding updating database supports automatically recalibrating, re-evaluating, and reselecting the evolving and improving algorithms.
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公开(公告)号：US11748820B1
公开(公告)日：2023-09-05
申请号：US17971584
申请日：2022-10-22
发明人： Jean P. Drouin , Samuel H. Bauknight , Todd Gottula , Yale Wang , Adam F. Rogow , Jeffrey D. Larson , Justin Warner , Erik Talvola
摘要： 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.
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公开(公告)号：US10910107B1
公开(公告)日：2021-02-02
申请号：US16845619
申请日：2020-04-10
摘要： A computer network architecture for a pipeline of models with machine learning and artificial intelligence for healthcare outcomes is presented. A machine learning prediction module and an artificial intelligence learning model are in electronic communication with a web application, which is also in electronic communication with a user device. An expanding updating database supports automatically recalibrating, re-evaluating, and reselecting the evolving and improving algorithms.
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公开(公告)号：US10811139B1
公开(公告)日：2020-10-20
申请号：US16007819
申请日：2018-06-13
摘要： Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and dynamic patient guidance. Embodiments automatically update patient guidance in the patient care plan, based on the effectiveness of the guidance to date, attributes of the patient, other updated information, ongoing experience of the network, and updated predictions of possible patient outcomes and metrics.
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公开(公告)号：US11636497B1
公开(公告)日：2023-04-25
申请号：US17586502
申请日：2022-01-27
发明人： Erik Talvola , Emmet Sun , Adam F. Rogow , Jeffrey D. Larson , Justin Warner
IPC分类号： G06Q30/02 , G06Q30/0201 , G06N5/02 , G06N20/00
摘要： 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.
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