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    Automatically evaluating likely accuracy of event annotations in field data
    5.
    发明授权
    Automatically evaluating likely accuracy of event annotations in field data 有权

    公开(公告)号：US10114807B2

    公开(公告)日：2018-10-30

    申请号：US13964811

    申请日：2013-08-12

    Applicant: Physio-Control, Inc.

    Inventor： Alexander Esibov ,  Isabelle Banville ,  Robert Marx ,  Fred Chapman ,  Randy Merry

    IPC: G06F17/20 ,  G06F17/24 ,  A61N1/39 ,  G06F17/27

    Abstract: Embodiments operate in contexts where field data have been generated from a field event, and annotations have been generated from the field data, which purport to identify events within the field data, such as CPR compressions and ventilations. Metrics are generated from the annotations, which are used in training. In such contexts, a grade may be assigned that reflects how well the annotations meet one or more accuracy criteria. The grade may be used in a number of ways. Reviewers may opt to disregard field data and metrics that have a low grade. Expert annotators may be guided as to precisely which annotations to revise, saving time. A low grade may decide that the results are not emailed to reviewers, but to annotators. A learning medical device can use the grade internally to adjust its own internal parameters so as to improve its annotating algorithms.

    AUTOMATICALLY EVALUATING LIKELY ACCURACY OF EVENT ANNOTATIONS IN FIELD DATA
    6.
    发明申请
    AUTOMATICALLY EVALUATING LIKELY ACCURACY OF EVENT ANNOTATIONS IN FIELD DATA 审中-公开
    在现场数据中自动评估事件注释的有效精度

    公开(公告)号：US20140047314A1

    公开(公告)日：2014-02-13

    申请号：US13964811

    申请日：2013-08-12

    Applicant: Physio-Control, Inc

    Inventor： Alexander Esibov ,  Isabelle Banville ,  Robert Marx ,  Fred Chapman ,  Randy Merry

    IPC: G06F17/24

    CPC classification number: G06F17/241 ,  A61N1/39044 ,  G06F17/27

    Abstract: Embodiments operate in contexts where field data have been generated from a field event, and annotations have been generated from the field data, which purport to identify events within the field data, such as CPR compressions and ventilations. Metrics are generated from the annotations, which are used in training. In such contexts, a grade may be assigned that reflects how well the annotations meet one or more accuracy criteria. The grade may be used in a number of ways. Reviewers may opt to disregard field data and metrics that have a low grade. Expert annotators may be guided as to precisely which annotations to revise, saving time. A low grade may decide that the results are not emailed to reviewers, but to annotators. A learning medical device can use the grade internally to adjust its own internal parameters so as to improve its annotating algorithms.

    Abstract translation: 实施例在从现场事件生成现场数据的情况下操作，并且已经从现场数据生成了注释，其旨在识别现场数据内的事件，例如CPR压缩和通风。 指标是从训练中使用的注释生成的。 在这种情况下，可以分配反映注释符合一个或多个准确性标准的程度的等级。 该等级可以以多种方式使用。 审核人员可以选择忽略具有低等级的现场数据和指标。 专家评论员可能会精确地指导哪些注释进行修改，从而节省时间。 低等级可能会决定结果不是通过电子邮件发送给审阅者，而是发送给注释者。 学习医疗器械可以在内部使用档次调整自己的内部参数，从而改进其注释算法。

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