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1.发明公开公开(公告)号：US20240047070A1
公开(公告)日：2024-02-08
申请号：US17817472
申请日：2022-08-04
申请人： Optum, Inc.
IPC分类号： G16H50/30
CPC分类号： G16H50/30
摘要： The present disclosure provides methods, apparatus, systems, computing devices, and/or the like for performing risk prediction by receiving outer cohort definition data and inner cohort definition data, the outer cohort definition data representative of a target data domain with respect to a dataset, and the inner cohort definition data representative of a prediction feature with respect to the target data domain, determining one or more inner cohort features based at least in part on a knowledge graph data object using the inner cohort definition data, the knowledge graph data object including co-occurrence information of features from the dataset, and generating, using a predictive machine learning model, for each of one or more outer cohort entities associated with features in an outer cohort data subset, a risk score representative of a propensity of the outer cohort entity being an inner cohort entity associated with features in an inner cohort data subset.
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公开(公告)号：US20220059230A1
公开(公告)日：2022-02-24
申请号：US16999133
申请日：2020-08-21
申请人： Optum, Inc.
摘要： Systems and methods are configured to perform machine-learning-based predictive behavioral response. In various embodiments, one or more behavioral monitoring data objects are identified and processed using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model. The behavioral pattern prediction model is processed using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part by one or more risk factors, and (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors. The risk model is processed using an adjustment generation machine learning model to generate an adjustment model and one or more prediction-based actions are performed based on the adjustment model.
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公开(公告)号：US20220122736A1
公开(公告)日：2022-04-21
申请号：US17225665
申请日：2021-04-08
申请人： Optum, Inc.
摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing risk score generation predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that risk score generation predictive data analysis by utilizing at least one of inferred hybrid risk score generation machine learning models and hybrid graph-based machine learning models.
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公开(公告)号：US11580113B2
公开(公告)日：2023-02-14
申请号：US16852938
申请日：2020-04-20
申请人： Optum, Inc.
IPC分类号： G06F16/2457 , G06F16/2453 , G06F16/248 , G06F16/435 , G16H70/40 , G06F9/451
摘要： Methods and systems for presenting a proactive audiovisual query using a virtual assistant software application. The methods correspond to retrieving user experience data associated with a user profile for the virtual assistant software application, wherein the user experience data define one or more user experience events for the user profile. The methods further include retrieving subject matter domain data associated with the one or more user experience events, wherein the subject matter domain data define one or more probabilistic event effects for the one or more user experience events and determining, based on the user experience data and the subject matter domain data, one or more optimal query items, wherein the one or more optimal query items are associated with at least one of the one or more probabilistic event effects. The methods further include generating the proactive audiovisual query in accordance with the one or more optimal query items.
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5.发明申请公开(公告)号：US20220327404A1
公开(公告)日：2022-10-13
申请号：US17382691
申请日：2021-07-22
申请人： Optum, Inc.
摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing risk score generation predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform risk score generation predictive data analysis by utilizing at least one of event-based confidence scores and delay-based confidence scores.
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公开(公告)号：US20210326344A1
公开(公告)日：2021-10-21
申请号：US16852938
申请日：2020-04-20
申请人： Optum, Inc.
IPC分类号： G06F16/2457 , G06F16/2453 , G06F16/248 , G06F9/451 , G06F16/435 , G16H70/40
摘要： Methods and systems for presenting a proactive audiovisual query using a virtual assistant software application. The methods correspond to retrieving user experience data associated with a user profile for the virtual assistant software application, wherein the user experience data define one or more user experience events for the user profile. The methods further include retrieving subject matter domain data associated with the one or more user experience events, wherein the subject matter domain data define one or more probabilistic event effects for the one or more user experience events and determining, based on the user experience data and the subject matter domain data, one or more optimal query items, wherein the one or more optimal query items are associated with at least one of the one or more probabilistic event effects. The methods further include generating the proactive audiovisual query in accordance with the one or more optimal query items.
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公开(公告)号：US12046372B2
公开(公告)日：2024-07-23
申请号：US16999133
申请日：2020-08-21
申请人： Optum, Inc.
摘要： Systems and methods are configured to perform machine-learning-based predictive behavioral response. In various embodiments, one or more behavioral monitoring data objects are identified and processed using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model. The behavioral pattern prediction model is processed using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part by one or more risk factors, and (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors. The risk model is processed using an adjustment generation machine learning model to generate an adjustment model and one or more prediction-based actions are performed based on the adjustment model.
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公开(公告)号：US11763946B2
公开(公告)日：2023-09-19
申请号：US16803465
申请日：2020-02-27
申请人： Optum, Inc.
发明人： Mark Gregory Megerian , Edward Sverdlin , Jonathan Lawrence Herke , Brent Mosier , Andrew Waiming Han
摘要： There is a need to perform predictive inference to predict likely adverse events of a drug regimen consisting of multiple drugs. In one example, a method includes determining, based at least in part on a graph-based predictive database, one or more predictive categories for each patient node of a plurality of patient nodes; determining, based at least in part on each one or more predictive categories for a patient node and each of one or more patient attribute nodes for a patient node, a related patient cohort for the primary patient node, wherein the related patient cohort comprises the primary patient node and one or more related patient nodes; determining, based at least in part on one or more intake relationships for each patient node in the related patient cohort, a first related drug profile for the primary patient node; and generating a first prediction interface based at least in part on the first related drug profile.
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公开(公告)号：US20210272693A1
公开(公告)日：2021-09-02
申请号：US16803465
申请日：2020-02-27
申请人： Optum, Inc.
发明人： Mark Gregory Megerian , Edward Sverdlin , Jonathan Lawrence Herke , Brent Mosier , Andrew Waiming Han
摘要： There is a need to perform predictive inference to predict likely adverse events of a drug regimen consisting of multiple drugs. In one example, a method includes determining, based at least in part on a graph-based predictive database, one or more predictive categories for each patient node of a plurality of patient nodes; determining, based at least in part on each one or more predictive categories for a patient node and each of one or more patient attribute nodes for a patient node, a related patient cohort for the primary patient node, wherein the related patient cohort comprises the primary patient node and one or more related patient nodes; determining, based at least in part on one or more intake relationships for each patient node in the related patient cohort, a first related drug profile for the primary patient node; and generating a first prediction interface based at least in part on the first related drug profile.
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