- 专利标题: Systems and methods for machine learning models for performance measurement
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申请号: US17209769申请日: 2021-03-23
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公开(公告)号: US11227184B1公开(公告)日: 2022-01-18
- 发明人: Nathaniel Freese , Meera Rao , Rick Wolf , Peyton Rose , Stephen Martin , Sameer Soi , Zachary Taylor , Ye Wang
- 申请人: Grand Rounds, Inc.
- 申请人地址: US CA San Francisco
- 专利权人: Grand Rounds, Inc.
- 当前专利权人: Grand Rounds, Inc.
- 当前专利权人地址: US CA San Francisco
- 代理机构: Finnegan, Henderson, Farabow, Garrett & Dunner, LLP
- 主分类号: G06K9/62
- IPC分类号: G06K9/62 ; G06N20/00 ; G06F16/242 ; G06F16/2458 ; G06Q30/02
摘要:
Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.
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