公开(公告)号：US20090313243A1

公开(公告)日：2009-12-17

申请号：US12324619

申请日：2008-11-26

申请人： Paul Buitelaar ,  Pinar Wennerberg ,  Sonja Zillner

发明人： Paul Buitelaar ,  Pinar Wennerberg ,  Sonja Zillner

IPC分类号： G06F7/06 ,  G06F17/30

CPC分类号： G06F16/367

摘要： A semantic data resource of a domain is processed by calculating relevance scores for terms which occur in domain corpora and weighting the semantic data resource depending on the relevance scores calculated for these terms. The semantic data resource may include domain-specific terms and relations, such as a domain ontology, a domain terminology and a domain classification. The domain ontology may include a domain-specific-hierarchy of terms assigned to nodes which are connected by edges and may be encoded in a web ontology language. The relevance scores may be chi-square scores which are calculated depending on a frequency of a term in the domain corpora and an expected frequency of the term.

摘要翻译： 通过计算域语料库中出现的术语的相关性分数来处理域的语义数据资源，并根据为这些术语计算的相关性分数对语义数据资源进行加权。 语义数据资源可以包括域特定的术语和关系，诸如域本体论，域术语和域分类。 域本体可以包括分配给通过边缘连接并且可以以网络本体语言编码的节点的项的域特定层级。 相关性分数可以是根据域语料库中的术语的频率和该术语的预期频率来计算的卡方分数。

公开(公告)号：US20160335544A1

公开(公告)日：2016-11-17

申请号：US14710380

申请日：2015-05-12

申请人： Claudia Bretschneider ,  Heiner Oberkampf ,  Sonja Zillner

发明人： Claudia Bretschneider ,  Heiner Oberkampf ,  Sonja Zillner

IPC分类号： G06N5/02 ,  G06F17/30

CPC分类号： G06N5/022 ,  G06F16/367 ,  G06F17/2785

摘要： A method for generating a knowledge data model is provided. The method includes providing at least one initial set of semantic type entities of a specific semantic type. The initial set of semantic type entities is expanded using available mappings between entities of the initial set and entities of unspecified type to generate an extended set of semantic type entities. Entities of a same semantic type are clustered within the extended set of semantic type entities. The method maps semantic relations between entities of different semantic type to relations between corresponding clusters containing the entities to generate the knowledge data model.

摘要翻译： 提供了一种用于生成知识数据模型的方法。 该方法包括提供特定语义类型的至少一个初始语义类型实体集合。 使用初始集合的实体和未指定类型的实体之间的可用映射来扩展初始语义类型实体集合，以生成扩展的语义类型实体集合。 相同语义类型的实体被聚集在扩展语义类型实体集合内。 该方法将不同语义类型的实体之间的语义关系映射到包含实体的对应集群之间的关系，以生成知识数据模型。

公开(公告)号：US20150095013A1

公开(公告)日：2015-04-02

申请号：US14038927

申请日：2013-09-27

申请人： Sonja Zillner ,  Claudia Bretschneider ,  Heiner Oberkampf

发明人： Sonja Zillner ,  Claudia Bretschneider ,  Heiner Oberkampf

IPC分类号： G06F17/27

CPC分类号： G06F17/2785 ,  G06F16/367 ,  G06F17/289

摘要： Concept labels of an ontology are extended. An ontology includes concepts at least partially authored in a source language; a corpus at least partially authored in a target language is provided; said corpus is processed by a linguistical analysis and receiving a list of first terms as a result of said linguistical analysis, said first terms in said list ordered by a linguistical relevancy; within said list, at least one of said first terms is associated with an associated second term, said second term being a translation of said first term into said source language; a retrieval is conducted by using at least one of said second terms for identifying a matching concept within said ontology; and a concept label of said matching concept is extended by a first term associated to said second term of a matching retrieval.

摘要翻译： 扩展本体的概念标签。 本体论包含至少部分以源语言编写的概念; 提供至少部分以目标语言撰写的语料库; 所述语料库由语言学分析处理，并且作为所述语言学分析的结果而接收第一术语的列表，所述列表中的第一个术语由语言相关性排序; 在所述列表内，所述第一项中的至少一个与相关联的第二项相关联，所述第二项是所述第一项到所述源语言的翻译; 通过使用用于识别所述本体内的匹配概念的所述第二项中的至少一个来进行检索; 并且所述匹配概念的概念标签通过与匹配检索的所述第二项相关联的第一项来扩展。

公开(公告)号：US20080201280A1

公开(公告)日：2008-08-21

申请号：US11763717

申请日：2007-06-15

申请人： Huber Martin ,  Alexey Tsymbal ,  Sonja Zillner

发明人： Huber Martin ,  Alexey Tsymbal ,  Sonja Zillner

IPC分类号： G06F15/18 ,  G06F17/00 ,  G06Q50/00

CPC分类号： G06N99/005 ,  G06F19/00 ,  G06Q50/24 ,  G16H50/20 ,  G16H50/70

摘要： A medical ontology may be used for computer assisted clinical decision support. Multi-level and/or semantically grouped medical ontology is incorporated into a machine learning algorithm. The resulting machine-learnt algorithm outputs information to assist in clinical decisions. For example, a patient record is input to the algorithm. Based on the incorporated medical ontology, similarities are aggregated in different groups. An aggregate similarity of at least one group is a function of an aggregate similarity of at least another group. One or more similar patients and/or outcomes are identified based on similarity. Probability based outputs may be provided.

摘要翻译： 医学本体可用于计算机辅助临床决策支持。 多级和/或语义分组的医学本体被并入机器学习算法中。 所得到的机器学习算法输出信息以辅助临床决策。 例如，患者记录被输入到算法中。 基于并入的医学本体，相似之处汇总在不同的组中。 至少一个组的聚合相似性是至少另一个组的聚合相似度的函数。 基于相似性来鉴定一种或多种类似的患者和/或结果。 可以提供基于概率的输出。

公开(公告)号：US20080027917A1

公开(公告)日：2008-01-31

申请号：US11767920

申请日：2007-06-25

申请人： Saikat Mukherjee ,  Shaohua Kevin Zhou ,  Xiang Zhou ,  Martin Huber ,  Jorg Freund ,  Volker Tresp ,  Sonja Zillner ,  Alok Gupta ,  Dorin Comaniciu

发明人： Saikat Mukherjee ,  Shaohua Kevin Zhou ,  Xiang Zhou ,  Martin Huber ,  Jorg Freund ,  Volker Tresp ,  Sonja Zillner ,  Alok Gupta ,  Dorin Comaniciu

IPC分类号： G06F17/30

CPC分类号： G06F16/583 ,  G06N5/022

摘要： A computer-implemented system for searching a plurality of images for an image of interest including a database of semantic image representations corresponding to the plurality of images, wherein the semantic image representations link a semantic model of clinical properties, a syntactic model of high level image properties and an image vocabulary of low level image properties, a set of queries associated with the semantic image representations, and a semantic search engine, embodied as computer readable code executed by a processor, for receiving a search query, selecting at least one of the set of queries based on the search query, and searching the plurality of images for the image of interest by comparing the plurality of images against the semantic image representations associated with a selected query.

摘要翻译： 一种用于搜索感兴趣的图像的多个图像的计算机实现的系统，包括与多个图像相对应的语义图像表示的数据库，其中所述语义图像表示链接临床属性的语义模型，高级图像的句法模型 低级图像属性的属性和图像词汇表，与语义图像表示相关联的一组查询，以及体现为由处理器执行的计算机可读代码的语义搜索引擎，用于接收搜索查询，选择至少一个 基于搜索查询的查询集合，以及通过将多个图像与与所选择的查询相关联的语义图像表示进行比较来搜索多个图像中的感兴趣图像。

公开(公告)号：US20150169833A1

公开(公告)日：2015-06-18

申请号：US14162670

申请日：2014-01-23

申请人： Ulli Waltinger ,  Sonja Zillner

发明人： Ulli Waltinger ,  Sonja Zillner

IPC分类号： G06F19/00

CPC分类号： G16H50/20 ,  G16H10/20 ,  G16H50/50

摘要： Medical experts are supported in the process of specifying and fine-tuning initial search requests by aggregating additional information about a patient context (e.g., patient, assumption, internal diagnose, external diagnose and procedure context). Mismatching information units are subsequently used as an entry point for improved and tailored information access by question answering systems. Different to traditional similarity-driven evidence ranking, an approach that does not disregard the mismatching information but emphasizes such silent signals is established.

摘要翻译： 在通过汇总关于患者上下文的附加信息（例如，患者，假设，内部诊断，外部诊断和程序上下文）来指定和微调初始搜索请求的过程中，支持医学专家。 随后将不匹配的信息单元用作问题回答系统改进和定制的信息访问的切入点。 与传统的相似驱动的证据排序不同，建立了不忽视不匹配信息但强调这种无声信号的方法。

公开(公告)号：US08965818B2

公开(公告)日：2015-02-24

申请号：US13473134

申请日：2012-05-16

申请人： Sonja Zillner ,  Heiner Oberkampf

发明人： Sonja Zillner ,  Heiner Oberkampf

IPC分类号： G06F15/18 ,  G06F17/00 ,  G06N7/00 ,  G06N7/08 ,  G06N5/04 ,  G06N5/00 ,  G06F19/00

CPC分类号： G06N5/00 ,  G06F19/00 ,  G16H50/20

摘要： Methods and systems may support conclusions of a clinician by enabling the clinician to arbitrarily define the symptoms according to the patient's record. The method may comprise: (a) determining a plurality of symptoms representing a patient and including characteristics of the patient, the symptoms being categorized as present symptoms, absent symptoms or open symptoms; (b) establishing a multi-tier relationship between said symptoms and a plurality of diseases based on a knowledge model; (c) adjusting weighting factor(s) for at least one multi-tier relationship based on said knowledge model; (d) optionally adjusting at least one weighting factor of step (c) by an expert; and (e) building a ranking of diseases from said multi-tier relationship based on said weighting factors. The method may provide an efficient ranking of disease information, thereby supporting a clinical diagnosis that may consider multiple symptoms while emphasizing particular symptoms in view of the clinician's expertise.

摘要翻译： 方法和系统可以通过使临床医生根据患者的记录任意地定义症状来支持临床医生的结论。 该方法可以包括：（a）确定代表患者的多种症状并包括患者的特征，症状被分类为现有症状，无症状或开放症状; （b）基于知识模型在所述症状与多种疾病之间建立多层关系; （c）基于所述知识模型调整至少一个多层关系的权重因子; （d）由专家任选地调整步骤（c）的至少一个加权因子; 以及（e）基于所述加权因子从所述多层关系建立疾病排名。 该方法可以提供疾病信息的有效排序，从而支持可以考虑多种症状的临床诊断，同时考虑到临床医生的专业知识强调特定症状。

公开(公告)号：US08935155B2

公开(公告)日：2015-01-13

申请号：US13619337

申请日：2012-09-14

申请人： Claudia Bretschneider ,  Sonja Zillner

发明人： Claudia Bretschneider ,  Sonja Zillner

IPC分类号： G06F17/27

CPC分类号： G06F17/2715 ,  G06F17/271 ,  G06F19/00 ,  G16H15/00

摘要： A method for processing medical reports includes applying natural language processing methods to perform an initial segmentation of at least parts of textual contents of each medical report into information units, thereby identifying information units, such as text fragments, sentences or text passages, within the medical reports. The information units are classified into at least one context class to determine their appropriate context classes for a particular situation or application. The context classifications are created using a grammar, e.g., a context-free grammar, and can then be used for automatically assigning each information unit to an appropriate context meta-information. The medical report may be annotated by assigning the information units to context meta-information determined by the context class, and the context meta-information may be used by other applications, e.g., dedicated mechanisms for clinical data integration, how to efficiently handle the available information units within medical reports.

摘要翻译： 一种用于处理医疗报告的方法包括：应用自然语言处理方法，将每个医疗报告的文本内容的至少一部分初始分割成信息单元，从而识别医学内的信息单元，例如文本片段，句子或文本段落 报告。 信息单元被分类为至少一个上下文类以确定其针对特定情况或应用的合适的上下文类。 使用语法（例如，无上下文的语法）创建上下文分类，然后可以用于将每个信息单元自动分配给适当的上下文元信息。 可以通过将信息单元分配给由上下文类确定的上下文元信息来注释医疗报告，并且上下文元信息可以被其他应用使用，例如用于临床数据集成的专用机制，如何有效地处理可用的 医疗报告中的信息单位。

公开(公告)号：US20140081623A1

公开(公告)日：2014-03-20

申请号：US13619337

申请日：2012-09-14

申请人： Claudia Bretschneider ,  Sonja Zillner

发明人： Claudia Bretschneider ,  Sonja Zillner

IPC分类号： G06F17/27

CPC分类号： G06F17/2715 ,  G06F17/271 ,  G06F19/00 ,  G16H15/00

摘要： A method for processing medical reports is disclosed. An initial segmentation textual contents of a medical report into information units is performed using natural language processing methods, thereby addressing the challenge of identifying information units, such as text fragments, sentences or text passages, covered within medical reports that are of relevance for a particular context. The information units are then classified into at least one context class to determine their appropriate context classes for a particular situation or application. The context classifications are created using a grammar, e.g., a context-free grammar, and can then be used for automatically assigning each information unit to an appropriate context meta-information. The medical report is then annotated by assigning at least one of said information units to at least one context meta-information determined by said context class. The context meta-information may be then used by other applications.

摘要翻译： 公开了一种处理医疗报告的方法。 使用自然语言处理方法将医疗报告的初始分段文本内容执行到信息单元中，从而解决识别信息单元（例如文本片段，句子或文本段落）的挑战，所述信息单元覆盖在医疗报告内，与特定的 上下文 然后，信息单元被分类为至少一个上下文类以确定其针对特定情况或应用的合适的上下文类。 使用语法（例如，无上下文的语法）创建上下文分类，然后可以用于将每个信息单元自动分配给适当的上下文元信息。 然后通过将至少一个所述信息单元分配给由所述上下文类确定的至少一个上下文元信息来注释医疗报告。 然后，上下文元信息可以由其他应用使用。

公开(公告)号：US08463816B2

公开(公告)日：2013-06-11

申请号：US13205135

申请日：2011-08-08

申请人： Michal Skubacz ,  Sonja Zillner

发明人： Michal Skubacz ,  Sonja Zillner

IPC分类号： G06F17/30

CPC分类号： G16H10/60 ,  G06F19/00

摘要： The method of administering a knowledge repository described herein establishes combining data input with concurrent data analytics in order to improve the quality and relevance of data entries.

摘要翻译： 管理本文所述的知识库的方法建立了将数据输入与并发数据分析相结合，以便提高数据条目的质量和相关性。