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1.发明公开公开(公告)号：US20240013064A1
公开(公告)日：2024-01-11
申请号：US17811229
申请日：2022-07-07
申请人： Optum, Inc.
发明人： Paul J. Godden , Erik A. Nystrom , Gregory J. Boss
IPC分类号： G06N3/12
CPC分类号： G06N3/126
摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a model deficiency data object for a tensor-based graph processing machine learning model. Certain embodiments of the present invention utilize systems, methods, and computer program products that generate a model deficiency data object for a tensor-based graph processing machine learning model using holistic graph links generated by utilizing a graph representation machine learning model.
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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：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：US20220367058A1
公开(公告)日：2022-11-17
申请号：US17318479
申请日：2021-05-12
申请人： Optum, Inc.
摘要： Various embodiments of the present invention utilize systems, methods, and computer program products that perform health-related predictive data analysis by utilizing an epistatic polygenic risk score generation machine learning model comprises at least one of the following: (i) an epistatic interaction score generation sub-model that is configured to process one or more significant epistatic interaction features for the patient data object that correspond to one or more significant epistatic interactions defined by the epistatic interaction score generation sub-model in order to generate an epistatic interaction score, and (ii) a base polygenic risk score generation sub-model that is configured to process one or more significant genetic variant features for the patient data object that correspond to one or more significant genetic variants defined by the base polygenic risk score generation machine learning model in order to generate a base polygenic risk score.
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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：US11977985B2
公开(公告)日：2024-05-07
申请号：US17096062
申请日：2020-11-12
申请人： Optum, Inc.
发明人： Darrel Naidoo , Paul J. Godden , Gregory J. Boss , Peter M. Wahl
IPC分类号： G06N3/0895 , G06F16/23 , G06N3/045 , G06N3/08 , G06N3/088 , G06N3/09 , G06N3/0985 , G06N20/00 , G16H50/20
CPC分类号： G06N3/0895 , G06F16/2379 , G06N3/045 , G06N3/08 , G06N3/088 , G06N3/09 , G06N3/0985 , G06N20/00 , G16H50/20
摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive prioritization. Certain embodiments utilize systems, methods, and computer program products that perform predictive prioritization using a combination of supervised machine learning models and unsupervised machine learning models that are in turn used to generate target features for a resultant prioritization machine learning model.
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7.发明公开公开(公告)号：US20240062052A1
公开(公告)日：2024-02-22
申请号：US17820681
申请日：2022-08-18
申请人： Optum, Inc.
发明人： Amit Kumar , Suman Roy , Ayan Sengupta , Paul J. Godden
IPC分类号： G06N3/04 , A61B5/00 , G06F16/901
CPC分类号： G06N3/049 , A61B5/7267 , G06F16/9024
摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a representative embeddings for a plurality of temporal sequences by using a graph attention augmented temporal network based at least in part on dynamic co-occurrence graphs for preceding temporal sequences and initial embeddings, where the dynamic co-occurrence graphs are projections of a global guidance co-occurrence graph on features of the preceding temporal sequences, and the initial embeddings are generated by processing a latent representation of corresponding features that is generated by a sequential long short term memory model as well as a feature tree using a tree-based long short term memory model.
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公开(公告)号：US20220165383A1
公开(公告)日：2022-05-26
申请号：US17100324
申请日：2020-11-20
申请人： Optum, Inc.
摘要： Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for causing a medication dispensing device to generate electronic user notifications. According to one embodiment, a method is provided that includes: receiving an attempt data object indicating an end user has communicated a medication access request for a medication; and responsive to receiving the object: receiving a predictive polypharmacy data object generated via processing an end user medication profile, an end user factor profile, and a polypharmacy profile for the medication using a polypharmacy prediction machine learning model indicating an inferred prediction about polypharmacy compatibility; determining, based at least in part on the predictive polypharmacy data object, a confirmation data object indicating a recommended response to the attempt data object; and transmitting the confirmation data object to the medical dispensing device to generate the electronic user notifications communicated to the end user.
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公开(公告)号：US20220147865A1
公开(公告)日：2022-05-12
申请号：US17096062
申请日：2020-11-12
申请人： Optum, Inc.
发明人： Darrel Naidoo , Paul J. Godden , Gregory J. Boss , Peter M. Wahl
摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive prioritization. Certain embodiments utilize systems, methods, and computer program products that perform predictive prioritization using a combination of supervised machine learning models and unsupervised machine learning models that are in turn used to generate target features for a resultant prioritization machine learning 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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