- 专利标题: Sequential minimal optimization algorithm for learning using partially available privileged information
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申请号: US16782573申请日: 2020-02-05
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公开(公告)号: US11531851B2公开(公告)日: 2022-12-20
- 发明人: Kayvan Najarian , Jonathan Gryak , Elyas Sabeti , Joshua Drews
- 申请人: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
- 申请人地址: US MI Ann Arbor
- 专利权人: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
- 当前专利权人: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
- 当前专利权人地址: US MI Ann Arbor
- 代理机构: Marshall, Gerstein & Borun LLP
- 主分类号: G06K9/62
- IPC分类号: G06K9/62 ; G16H50/50 ; G06N20/10
摘要:
Computational algorithms integrate and analyze data to consider multiple interdependent, heterogeneous sources and forms of patient data, and using a classification model, provide new learning paradigms, including privileged learning and learning with uncertain clinical data, to determine patient status for conditions such as acute respiratory distress syndrome (ARDS) or non-ARDS.
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