- 专利标题: MACHINE-LEARNING-BASED PREDICTIVE BEHAVIORIAL MONITORING
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申请号: US16999133申请日: 2020-08-21
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公开(公告)号: US20220059230A1公开(公告)日: 2022-02-24
- 发明人: Jon Kevin Muse , Gregory J. Boss , Paul J. Godden , Mark Gregory Megerian
- 申请人: Optum, Inc.
- 申请人地址: US MN Minnetonka
- 专利权人: Optum, Inc.
- 当前专利权人: Optum, Inc.
- 当前专利权人地址: US MN Minnetonka
- 主分类号: G16H50/30
- IPC分类号: G16H50/30 ; G16H50/20
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.
公开/授权文献
- US12046372B2 Machine-learning-based predictive behavioral monitoring 公开/授权日:2024-07-23
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