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1. 发明申请

US20200233928A1 PREDICTIVE NATURAL LANGUAGE PROCESSING USING SEMANTIC FEATURE EXTRACTION 审中-公开
公开(公告)号：US20200233928A1
公开(公告)日：2020-07-23
申请号：US16253886
申请日：2019-01-22
申请人： Optum, Inc.
发明人： Christopher A. Hane , Vijay S. Nori , Louis J. Rumanes
IPC分类号： G06F17/27
摘要： There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy. This need can be addressed by, for example, by identifying an indexed representation of a natural language object; obtaining a vocabulary domain associated with one or more first phrases; determining an individual frequency for each first phrase based on a count of occurrences of the first phrase in the indexed representation; identifying one or more dominant phrases of the first phrases; for each dominant phrase, identifying any dependent phrases for the first dominant phrase; determining a semantically-adjusted frequency for each dominant phrase based on the individual frequency for the dominant phrase and each individual frequency for any dependent phrase for the dominant phrase; generating a structured representation of the natural language object based on each semantically-adjusted frequency associated with a dominant phrase; and providing the structured representation for the predictive analysis.

2. 发明授权

US11537818B2 Apparatus, computer program product, and method for predictive data labelling using a dual-prediction model system 有权
公开(公告)号：US11537818B2
公开(公告)日：2022-12-27
申请号：US16746232
申请日：2020-01-17
申请人： Optum, Inc.
发明人： Vijay S. Nori , Christopher A. Hane , Paul A. Bleicher
IPC分类号： G06K9/00 , G06K9/62 , G06F17/16 , G06N20/00
摘要： Various embodiments of the disclosure provide apparatuses, systems, and computer program products for predictive data labelling using a dual-model system. Embodiments provide various advantages in accuracy of predicted labels, for example in various contexts such as medical data analysis for difficult to diagnose diseases. An example provided apparatus is configured to generate a positive, neutral, and negative candidate identifier sets and corresponding positive, neutral, and negative candidate index sets based in part on applying a candidate selection rule set to a candidate data set; train a candidate label probabilistic model based at least in part on a candidate label training subset associated with the candidate data set associated with the positive and negative candidate identifiers; generate a candidate positive-label probability set using at least the candidate label probabilistic model; train a historical record prediction model to predict the candidate positive-label probability set; and utilize the historical record prediction model.

3. 发明申请

US20210294981A1 PREDICTIVE NATURAL LANGUAGE PROCESSING USING SEMANTIC FEATURE EXTRACTION 有权
公开(公告)号：US20210294981A1
公开(公告)日：2021-09-23
申请号：US17303678
申请日：2021-06-04
申请人： OPTUM, INC.
发明人： Christopher A. Hane , Vijay S. Nori , Louis J. Rumanes
IPC分类号： G06F40/30
摘要： There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy. This need can be addressed by, for example, by identifying an indexed representation of a natural language object; obtaining a vocabulary domain associated with one or more first phrases; determining an individual frequency for each first phrase based on a count of occurrences of the first phrase in the indexed representation; identifying one or more dominant phrases of the first phrases; for each dominant phrase, identifying any dependent phrases for the first dominant phrase; determining a semantically-adjusted frequency for each dominant phrase based on the individual frequency for the dominant phrase and each individual frequency for any dependent phrase for the dominant phrase; generating a structured representation of the natural language object based on each semantically-adjusted frequency associated with a dominant phrase; and providing the structured representation for the predictive analysis.

4. 发明申请

US20140280309A1 Efficient SQL Based Multi-Attribute Clustering 有权
标题翻译：高效的基于SQL的多属性聚类
公开(公告)号：US20140280309A1
公开(公告)日：2014-09-18
申请号：US13843441
申请日：2013-03-15
申请人： OPTUM, INC.
发明人： David R. Anderson , Christopher A. Hane
IPC分类号： G06F17/30
CPC分类号： G06F17/30598 , G06F17/245 , G06F17/30292 , G06F17/30312 , G06F17/30427 , G06F17/30595 , G06Q30/02
摘要： Efficient SQL based multi-attribute clustering of data attributes may be used to identify the most relevant combination of data attributes to an outcome. A global outcome value may be calculated to represent an average of the outcome. A subset outcome value for each subset of data attributes of a plurality of attributes may be calculated to represent average of the outcome for the subset. For each subset of data attributes, a number of members associated with the subset may be compared to a threshold, and the subsets with less members than the threshold may be removed. The subset outcome value for each subset of data attributes may be compared to the global outcome value, and a report may be generated that identifies each subset for which the corresponding subset outcome value is greater than or less than the global outcome value.
摘要翻译：数据属性的高效的基于SQL的多属性聚类可用于识别数据属性与结果最相关的组合。可以计算全局结果值来表示结果的平均值。可以计算多个属性的数据属性的每个子集的子集结果值，以表示子集的结果的平均值。对于数据属性的每个子集，可以将与子集相关联的多个成员与阈值进行比较，并且可以去除具有小于阈值的成员的子集。可以将数据属性的每个子集的子集结果值与全局结果值进行比较，并且可以生成识别相应子集结果值大于或小于全局结果值的每个子集的报告。

5. 发明申请

US20210294982A1 PREDICTIVE NATURAL LANGUAGE PROCESSING USING SEMANTIC FEATURE EXTRACTION 有权
公开(公告)号：US20210294982A1
公开(公告)日：2021-09-23
申请号：US17303679
申请日：2021-06-04
申请人： OPTUM, INC.
发明人： Christopher A. Hane , Vijay S. Nori , Louis J. Rumanes
IPC分类号： G06F40/30
摘要： There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy. This need can be addressed by, for example, by identifying an indexed representation of a natural language object; obtaining a vocabulary domain associated with one or more first phrases; determining an individual frequency for each first phrase based on a count of occurrences of the first phrase in the indexed representation; identifying one or more dominant phrases of the first phrases; for each dominant phrase, identifying any dependent phrases for the first dominant phrase; determining a semantically-adjusted frequency for each dominant phrase based on the individual frequency for the dominant phrase and each individual frequency for any dependent phrase for the dominant phrase; generating a structured representation of the natural language object based on each semantically-adjusted frequency associated with a dominant phrase; and providing the structured representation for the predictive analysis.

6. 发明授权

US11055490B2 Predictive natural language processing using semantic feature extraction 有权
公开(公告)号：US11055490B2
公开(公告)日：2021-07-06
申请号：US16253886
申请日：2019-01-22
申请人： Optum, Inc.
发明人： Christopher A. Hane , Vijay S. Nori , Louis J. Rumanes
IPC分类号： G06F40/30
摘要： There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy. This need can be addressed by, for example, by identifying an indexed representation of a natural language object; obtaining a vocabulary domain associated with one or more first phrases; determining an individual frequency for each first phrase based on a count of occurrences of the first phrase in the indexed representation; identifying one or more dominant phrases of the first phrases; for each dominant phrase, identifying any dependent phrases for the first dominant phrase; determining a semantically-adjusted frequency for each dominant phrase based on the individual frequency for the dominant phrase and each individual frequency for any dependent phrase for the dominant phrase; generating a structured representation of the natural language object based on each semantically-adjusted frequency associated with a dominant phrase; and providing the structured representation for the predictive analysis.

7. 发明授权

US09195732B2 Efficient SQL based multi-attribute clustering 有权
标题翻译：高效的基于SQL的多属性聚类
公开(公告)号：US09195732B2
公开(公告)日：2015-11-24
申请号：US13843441
申请日：2013-03-15
申请人： Optum, Inc.
发明人： David R. Anderson , Christopher A. Hane
IPC分类号： G06F17/30 , G06F7/00 , G06F17/24 , G06Q30/02
CPC分类号： G06F17/30598 , G06F17/245 , G06F17/30292 , G06F17/30312 , G06F17/30427 , G06F17/30595 , G06Q30/02
摘要： Efficient SQL based multi-attribute clustering of data attributes may be used to identify the most relevant combination of data attributes to an outcome. A global outcome value may be calculated to represent an average of the outcome. A subset outcome value for each subset of data attributes of a plurality of attributes may be calculated to represent average of the outcome for the subset. For each subset of data attributes, a number of members associated with the subset may be compared to a threshold, and the subsets with less members than the threshold may be removed. The subset outcome value for each subset of data attributes may be compared to the global outcome value, and a report may be generated that identifies each subset for which the corresponding subset outcome value is greater than or less than the global outcome value.
摘要翻译：数据属性的高效的基于SQL的多属性聚类可用于识别数据属性与结果最相关的组合。可以计算全局结果值来表示结果的平均值。可以计算多个属性的数据属性的每个子集的子集结果值，以表示子集的结果的平均值。对于数据属性的每个子集，可以将与子集相关联的多个成员与阈值进行比较，并且可以去除具有小于阈值的成员的子集。可以将数据属性的每个子集的子集结果值与全局结果值进行比较，并且可以生成识别相应子集结果值大于或小于全局结果值的每个子集的报告。

8. 发明申请

US20210294983A1 PREDICTIVE NATURAL LANGUAGE PROCESSING USING SEMANTIC FEATURE EXTRACTION 有权
公开(公告)号：US20210294983A1
公开(公告)日：2021-09-23
申请号：US17303680
申请日：2021-06-04
申请人： OPTUM, INC.
发明人： Christopher A. Hane , Vijay S. Nori , Louis J. Rumanes
IPC分类号： G06F40/30
摘要： There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy. This need can be addressed by, for example, by identifying an indexed representation of a natural language object; obtaining a vocabulary domain associated with one or more first phrases; determining an individual frequency for each first phrase based on a count of occurrences of the first phrase in the indexed representation; identifying one or more dominant phrases of the first phrases; for each dominant phrase, identifying any dependent phrases for the first dominant phrase; determining a semantically-adjusted frequency for each dominant phrase based on the individual frequency for the dominant phrase and each individual frequency for any dependent phrase for the dominant phrase; generating a structured representation of the natural language object based on each semantically-adjusted frequency associated with a dominant phrase; and providing the structured representation for the predictive analysis.

9. 发明授权

US10891352B1 Code vector embeddings for similarity metrics 有权
公开(公告)号：US10891352B1
公开(公告)日：2021-01-12
申请号：US15927188
申请日：2018-03-21
申请人： Optum, Inc.
发明人： Christopher A. Hane , Alexander Kravetz
IPC分类号： G06F17/16 , G06K9/62 , G16H10/60 , G06N20/00 , G06F40/157
摘要： Aggregate vectors corresponding to non-textual information/data are provided in a multi-dimensional space. A computing entity access a plurality of instances of medical information comprising medical codes. The computing entity generates one or more medical sentences from the plurality of instances of medical information. Each medical sentence comprises one or more medical codes. The computing entity generates an embedding vector dictionary comprising a plurality of multi-dimensional vectors based on a medical embedding model trained using machine learning and the one or more medical sentences. Each multi-dimensional vector corresponds to a medical code. The computing entity generates a plurality of aggregate vectors based on the embedding vector dictionary and analyzes at least a portion of the plurality of aggregate vectors to identify two or more aggregate vectors that are similar or different based on a distance between the two or more aggregate vectors in the multi-dimensional space.

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