Abstract: A method for supporting an acquisition of clinical data may include the steps of: receiving, by an input component, an identifier characterizing a health state of a patient; accessing a diagnose process model associated to said identifier, the diagnose process model specifying a sequence of clinical treatments whereby each of said clinical treatments includes at least one information model; sequentially activating information models from at least one of said clinical treatments by said process model; requesting, by each of said activated information models, at least one data unit; extracting an information unit by applying an information extraction method to said data unit and interpreting the information unit by reasoning and mapping the information unit to at least one knowledge model; and sequentially instantiating at least one information model by a plurality of said information units.

Abstract: 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.

Abstract: A method and system for supporting a clinical diagnosis is provided, the method including the steps of representing a patient by a plurality of initial symptoms, the symptoms including characteristics of the patient; determining, by querying and reasoning in a disease-symptom knowledge model, at least one likely disease for each of the plurality of initial symptoms; mapping the plurality of initial symptoms onto a set of said likely diseases; determining for each disease of said set of likely diseases a plurality of associated symptoms, the associated symptoms being associated to a respective disease of said set of likely diseases in said disease-symptom knowledge model; categorizing each of the plurality of associated symptoms into disjoint categories by mapping associated symptoms with initial symptoms; and representing likely diseases in the context of said categories of associated symptoms.

Abstract: A modular method of composing an ontology alignment provides a set of correspondences between at least two ontologies thereby allowing a composition of an optimal alignment by balancing a recall value and a precision of the alignment. A two-fold strategy is followed. By means of mapping functions a set of alignment correspondences is determined. Depending on a particular mapping function a recall value of the alignment can be improved by an extension of the set of correspondences. By filtering functions particularities of the domain are reflected and incorrect mappings are avoided. Depending on a particular filtering function a precision value of the alignment can be improved by restricting the set of correspondences.

Abstract: 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.

Abstract: 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.

Abstract: A method composing a medical image analysis, includes the steps of: retrieving anatomical findings captured in a medical report by an information extraction module; providing at least one anatomical knowledge model including a plurality of concepts and a plurality of relationship between the plurality of concepts; mapping at least one anatomical finding of the medical re-port by at least one of the plurality of concepts by a mapping component, thereby locating the anatomical findings in the context of the anatomical knowledge model; and; representing the anatomical findings in the context of the anatomical knowledge model within an anatomical model by a visualization component.

Abstract: A modular method of composing an ontology alignment provides a set of correspondences between at least two ontologies thereby allowing a composition of an optimal alignment by balancing a recall value and a precision of the alignment. A two-fold strategy is followed. By means of mapping functions a set of alignment correspondences is determined. Depending on a particular mapping function a recall value of the alignment can be improved by an extension of the set of correspondences. By filtering functions particularities of the domain are reflected and incorrect mappings are avoided. Depending on a particular filtering function a precision value of the alignment can be improved by restricting the set of correspondences.

Abstract: 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.

Abstract: A method of retrieving information regarding an information resource, has the steps of: providing an information resource with a plurality of information instances from a first memory unit; providing a knowledge model with a plurality of type categories and a relationship between at least two type categories from a second memory unit; providing a multi-dimensional context model with a plurality of context categories and a number of dimensions corresponding to a number of the context categories from a third memory unit; annotating an information instance by one of the type categories; mapping at least one type category and/or annotating at least one information instance by one of the context categories; and; assigning a context variable to one of the context categories, the context variable determining a value of a context distance between the context category and the information instance mapped to the context category.

Abstract: A method and system for providing instance information data of at least one instance. The method has the steps of generating a semantic representation of the instance on the basis of at least one instance data source, providing a semantic representation of a domain on the basis of at least one structured domain data source, performing an integration of the semantic instance and domain representations to provide an integrated semantic representation and deriving automatically instance information data from the provided integrated semantic representation. The method and system allow to provide efficiently instance information data of a wide range of instances with technical entities and persons, in a particular patients in different domains such as radiology or anatomy.

Abstract: 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.

Abstract: A method and an apparatus provide at least one configuration data ontology module. Modules are extracted from an ontology under consideration of the semantics of the ontology. Therefore, individual concepts are selected from the ontology which are in a subsequent step connected by relations. The method and apparatus are used in ontology modularization, and especially in biomedical application domains.

Abstract: A method and an apparatus retrieve additional information regarding a patient record. Applying the subject-matter of the present invention clinical experts, such as doctors, can be provided with domain specific background knowledge, and can, hence, be supported in making decisions regarding the treatment of patients. Said additional information can be structured and visualised, which makes the retrieved information understandable also for persons without advanced knowledge regarding data processing. The retrieval of additional information is accomplished by a comparison of data being stored in the patient record and data being stored in a predefined textual resource, such as a ontology, and an identification of further terms describing attributes of the patient record. This finds application in supporting the diagnosis of patients and healthcare related information retrieval tasks.

Abstract: 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.

Abstract: 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.

Abstract: 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.