公开(公告)号：US10198431B2

公开(公告)日：2019-02-05

申请号：US13214291

申请日：2011-08-22

申请人： Swapna Somasundaran ,  Dingcheng Li ,  Amit Chakraborty

发明人： Swapna Somasundaran ,  Dingcheng Li ,  Amit Chakraborty

IPC分类号： G06F17/30 ,  G06F17/27

摘要： For generating a word space, manual thresholding of word scores is used. Rather than requiring the user to select the threshold arbitrarily or review each word, the user is iteratively requested to indicate the relevance of a given word. Words with greater or lesser scores are labeled in the same way depending upon the response. For determining the relationship between named entities, Latent Dirichlet Allocation (LDA) is performed on text associated with the name entities rather than on an entire document. LDA for relationship mining may include context information and/or supervised learning.

公开(公告)号：US08700589B2

公开(公告)日：2014-04-15

申请号：US13479388

申请日：2012-05-24

申请人： Kateryna Tymoshenko ,  Swapna Somasundaran ,  Vinay Damodar Shet

发明人： Kateryna Tymoshenko ,  Swapna Somasundaran ,  Vinay Damodar Shet

IPC分类号： G06F17/21 ,  G06F17/30 ,  G06F17/00

CPC分类号： G06F17/30705 ,  G06F19/00 ,  G16H50/20 ,  G16H50/70 ,  Y10S707/99933

摘要： A system generates medical knowledge base information by using predetermined data source specific message syntax information in identifying first and second information received from first and second data sources respectively. The first and second information indicates at least one type of medical relationship between the received first and second medical terms. The system determines likelihood of existence of the at least one type of medical relationship indicated by a combination of the first and second information, in response to predetermined information indicating a number of occurrences of the at least one type of relationship in data of at least one of the first and second data source. The system outputs first and second medical terms and the at least one type of medical relationship in response to the determined likelihood of existence.

摘要翻译： 系统通过使用预定的数据源特定消息语法信息分别识别从第一和第二数据源接收的第一和第二信息来生成医学知识库信息。 第一和第二信息指示接收到的第一和第二医疗术语之间的至少一种类型的医疗关系。 响应于指示至少一种数据中的至少一种类型的关系的出现次数的预定信息，该系统确定存在由第一和第二信息的组合指示的至少一种类型的医疗关系的可能性 的第一和第二数据源。 该系统响应确定的存在的可能性而输出第一和第二医疗术语和至少一种类型的医疗关系。

公开(公告)号：US08639678B2

公开(公告)日：2014-01-28

申请号：US13479363

申请日：2012-05-24

申请人： Swapna Somasundaran ,  Vinodkumar Prabhakaran ,  Vinay Damodar Shet ,  Kateryna Tymoshenko ,  Mathäus Dejori

发明人： Swapna Somasundaran ,  Vinodkumar Prabhakaran ,  Vinay Damodar Shet ,  Kateryna Tymoshenko ,  Mathäus Dejori

IPC分类号： G06F17/30

CPC分类号： G06F17/30705 ,  G06F19/00 ,  G16H50/20 ,  G16H50/70 ,  Y10S707/99933

摘要： A system generates medical knowledge base information by searching at least one repository of medical information to identify sentences including a received medical term. A data processor searches the identified sentences to identify sentences including a medical term different to the received term in response to a predetermined repository of medical terms and excludes sentences without a term different to the received term, to provide remaining multiple term sentences. The data processor groups different terms of individual sentences of the multiple term sentences to provide grouped terms, determines whether a medically valid relationship occurs between different terms of an individual group of terms of the grouped terms by using predetermined sentence structure and syntax rules and outputs data representing grouped terms having a medically valid relationship.

摘要翻译： 系统通过搜索至少一个医疗信息库来识别包括接收到的医疗术语的句子来生成医学知识库信息。 数据处理器搜索所识别的句子以响应于医学术语的预定存储库来识别包括与接收到的术语不同的医学术语的句子，并且排除不具有与接收到的术语不同的术语的句子，以提供剩余的多个句子句子。 数据处理器对多个句子的单个句子的不同术语进行分组以提供分组的术语，通过使用预定的句子结构和语法规则来确定分组术语的单个术语组的不同术语之间是否发生医学上有效的关系，并输出数据 代表具有医学上有效关系的分组术语。

公开(公告)号：US20130066903A1

公开(公告)日：2013-03-14

申请号：US13479388

申请日：2012-05-24

申请人： Kateryna Tymoshenko ,  Swapna Somasundaran ,  Vinay Damodar Shet

发明人： Kateryna Tymoshenko ,  Swapna Somasundaran ,  Vinay Damodar Shet

IPC分类号： G06F17/30

CPC分类号： G06F17/30705 ,  G06F19/00 ,  G16H50/20 ,  G16H50/70 ,  Y10S707/99933

摘要： A system generates medical knowledge base information by using predetermined data source specific message syntax information in identifying first and second information received from first and second data sources respectively. The first and second information indicates at least one type of medical relationship between the received first and second medical terms. The system determines likelihood of existence of the at least one type of medical relationship indicated by a combination of the first and second information, in response to predetermined information indicating a number of occurrences of the at least one type of relationship in data of at least one of the first and second data source. The system outputs first and second medical terms and the at least one type of medical relationship in response to the determined likelihood of existence.

摘要翻译： 系统通过使用预定的数据源特定消息语法信息分别识别从第一和第二数据源接收的第一和第二信息来生成医学知识库信息。 第一和第二信息指示接收到的第一和第二医疗术语之间的至少一种类型的医疗关系。 响应于指示至少一种数据中的至少一种类型的关系的出现次数的预定信息，该系统确定存在由第一和第二信息的组合指示的至少一种类型的医疗关系的可能性 的第一和第二数据源。 该系统响应确定的存在的可能性而输出第一和第二医疗术语和至少一种类型的医疗关系。

公开(公告)号：US20130066870A1

公开(公告)日：2013-03-14

申请号：US13479363

申请日：2012-05-24

申请人： Swapna Somasundaran ,  Vinodkumar Prabhakaran ,  Vinay Damodar Shet ,  Kateryna Tymoshenko ,  Mathäus Dejori

发明人： Swapna Somasundaran ,  Vinodkumar Prabhakaran ,  Vinay Damodar Shet ,  Kateryna Tymoshenko ,  Mathäus Dejori

IPC分类号： G06F17/30

CPC分类号： G06F17/30705 ,  G06F19/00 ,  G16H50/20 ,  G16H50/70 ,  Y10S707/99933

摘要： A system generates medical knowledge base information by searching at least one repository of medical information to identify sentences including a received medical term. A data processor searches the identified sentences to identify sentences including a medical term different to the received term in response to a predetermined repository of medical terms and excludes sentences without a term different to the received term, to provide remaining multiple term sentences. The data processor groups different terms of individual sentences of the multiple term sentences to provide grouped terms, determines whether a medically valid relationship occurs between different terms of an individual group of terms of the grouped terms by using predetermined sentence structure and syntax rules and outputs data representing grouped terms having a medically valid relationship.

摘要翻译： 系统通过搜索至少一个医疗信息库来识别包括接收到的医疗术语的句子来生成医学知识库信息。 数据处理器搜索所识别的句子以响应于医学术语的预定存储库来识别包括与接收到的术语不同的医学术语的句子，并且排除不具有与接收到的术语不同的术语的句子，以提供剩余的多个句子句子。 数据处理器对多个句子的单个句子的不同术语进行分组以提供分组的术语，通过使用预定的句子结构和语法规则来确定分组术语的单个术语组的不同术语之间是否发生医学上有效的关系，并输出数据 代表具有医学上有效关系的分组术语。

公开(公告)号：US20120078918A1

公开(公告)日：2012-03-29

申请号：US13214291

申请日：2011-08-22

申请人： Swapna Somasundaran ,  Dingcheng Li ,  Amit Chakraborty

发明人： Swapna Somasundaran ,  Dingcheng Li ,  Amit Chakraborty

IPC分类号： G06F17/30

CPC分类号： G06F17/278 ,  G06F17/277 ,  G06F17/30707 ,  G06F17/30731

摘要： For generating a word space, manual thresholding of word scores is used. Rather than requiring the user to select the threshold arbitrarily or review each word, the user is iteratively requested to indicate the relevance of a given word. Words with greater or lesser scores are labeled in the same way depending upon the response. For determining the relationship between named entities, Latent Dirichlet Allocation (LDA) is performed on text associated with the name entities rather than on an entire document. LDA for relationship mining may include context information and/or supervised learning.

摘要翻译： 为了产生一个单词空间，使用手动阈值词分数。 不需要用户任意选择阈值或查看每个单词，反复请求用户来指示给定单词的相关性。 具有更大或更小成绩的词根据回应以相同的方式标记。 为了确定命名实体之间的关系，在与名称实体相关联的文本上执行潜在狄更特分配（LDA），而不是整个文档。 关系挖掘的LDA可能包括上下文信息和/或监督学习。