�ݺ�ߣshows by User: Snow

�ݺ�ߣshows by User: Snow_Owl / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: Snow_Owl / Fri, 05 Dec 2014 08:34:15 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: Snow_Owl Snow Owl Platform. Unlocking the meaning from healthcare data. /slideshow/snow-owl-platform-unlocking-the-meaning-of-healthcare-data/42394433 snowowlintro-141205083415-conversion-gate02
This talk discusses our implementation experience adding SNOMED CT support into Snow Owl, a terminology authoring platform. Aspects of the platform will be demonstrated that addressed challenges such as: implementing the HL7 TermInfo standard to support semantic search of SNOMED CT concepts; implementation of SNOMED CT RF2 query-based (intensional) reference sets; benefits of using off-the-shelf description logic classifiers (including ELK and FaCT++) to identify logical errors in SNOMED CT concept definitions; support for collaborative authoring via task management; and multi-user distributed authoring. Please see our website http://b2i.sg for further information.]]>
This talk discusses our implementation experience adding SNOMED CT support into Snow Owl, a terminology authoring platform. Aspects of the platform will be demonstrated that addressed challenges such as: implementing the HL7 TermInfo standard to support semantic search of SNOMED CT concepts; implementation of SNOMED CT RF2 query-based (intensional) reference sets; benefits of using off-the-shelf description logic classifiers (including ELK and FaCT++) to identify logical errors in SNOMED CT concept definitions; support for collaborative authoring via task management; and multi-user distributed authoring. Please see our website http://b2i.sg for further information.]]> Fri, 05 Dec 2014 08:34:15 GMT /slideshow/snow-owl-platform-unlocking-the-meaning-of-healthcare-data/42394433 Snow_Owl@slideshare.net(Snow_Owl) Snow Owl Platform. Unlocking the meaning from healthcare data. Snow_Owl This talk discusses our implementation experience adding SNOMED CT support into Snow Owl, a terminology authoring platform. Aspects of the platform will be demonstrated that addressed challenges such as: implementing the HL7 TermInfo standard to support semantic search of SNOMED CT concepts; implementation of SNOMED CT RF2 query-based (intensional) reference sets; benefits of using off-the-shelf description logic classifiers (including ELK and FaCT++) to identify logical errors in SNOMED CT concept definitions; support for collaborative authoring via task management; and multi-user distributed authoring. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/snowowlintro-141205083415-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This talk discusses our implementation experience adding SNOMED CT support into Snow Owl, a terminology authoring platform. Aspects of the platform will be demonstrated that addressed challenges such as: implementing the HL7 TermInfo standard to support semantic search of SNOMED CT concepts; implementation of SNOMED CT RF2 query-based (intensional) reference sets; benefits of using off-the-shelf description logic classifiers (including ELK and FaCT++) to identify logical errors in SNOMED CT concept definitions; support for collaborative authoring via task management; and multi-user distributed authoring. Please see our website http://b2i.sg for further information.

Snow Owl Platform. Unlocking the meaning from healthcare data. from Snow Owl

]]> 1706 10 https://cdn.slidesharecdn.com/ss_thumbnails/snowowlintro-141205083415-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 Implementing reusable software components for SNOMED CT diagram and expression concept representations /slideshow/implementing-reusable-softwarecomponentsmie2014/42392257 implementingreusablesoftwarecomponentsmie2014-141205073336-conversion-gate02
SNOMED CT is a vital component in the future of semantic interoperability in healthcare as it provides the meaning to EHRs via its semantically rich, controlled terminology. Communicating the concepts of this terminology to both humans and machines is crucial therefore formal guidelines for diagram and expression representations have been developed by the curators of SNOMED CT. This paper presents a novel, model-based approach to implementing these guidelines that allows simultaneous editing of a concept via both diagram and expression editors. The implemented extensible software component can be embedded both both desktop and web applications. Please see our website http://b2i.sg for further information.]]>
SNOMED CT is a vital component in the future of semantic interoperability in healthcare as it provides the meaning to EHRs via its semantically rich, controlled terminology. Communicating the concepts of this terminology to both humans and machines is crucial therefore formal guidelines for diagram and expression representations have been developed by the curators of SNOMED CT. This paper presents a novel, model-based approach to implementing these guidelines that allows simultaneous editing of a concept via both diagram and expression editors. The implemented extensible software component can be embedded both both desktop and web applications. Please see our website http://b2i.sg for further information.]]> Fri, 05 Dec 2014 07:33:36 GMT /slideshow/implementing-reusable-softwarecomponentsmie2014/42392257 Snow_Owl@slideshare.net(Snow_Owl) Implementing reusable software components for SNOMED CT diagram and expression concept representations Snow_Owl SNOMED CT is a vital component in the future of semantic interoperability in healthcare as it provides the meaning to EHRs via its semantically rich, controlled terminology. Communicating the concepts of this terminology to both humans and machines is crucial therefore formal guidelines for diagram and expression representations have been developed by the curators of SNOMED CT. This paper presents a novel, model-based approach to implementing these guidelines that allows simultaneous editing of a concept via both diagram and expression editors. The implemented extensible software component can be embedded both both desktop and web applications. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/implementingreusablesoftwarecomponentsmie2014-141205073336-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> SNOMED CT is a vital component in the future of semantic interoperability in healthcare as it provides the meaning to EHRs via its semantically rich, controlled terminology. Communicating the concepts of this terminology to both humans and machines is crucial therefore formal guidelines for diagram and expression representations have been developed by the curators of SNOMED CT. This paper presents a novel, model-based approach to implementing these guidelines that allows simultaneous editing of a concept via both diagram and expression editors. The implemented extensible software component can be embedded both both desktop and web applications. Please see our website http://b2i.sg for further information.

Implementing reusable software components for SNOMED CT diagram and expression concept representations from Snow Owl

]]> 1777 3 https://cdn.slidesharecdn.com/ss_thumbnails/implementingreusablesoftwarecomponentsmie2014-141205073336-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 Implementing an HL7 version 3 modeling tool from an Ecore model /slideshow/implementing-an-hl7version3modelingtoolfromanecoremodel/42391637 implementinganhl7version3modelingtoolfromanecoremodel-141205071323-conversion-gate01
One of the main challenges of achieving interoperability using the HL7 V3 healthcare standard is the lack of clear definition and supporting tools for modeling, testing, and conformance checking. Currently, the knowledge defining the modeling is scattered around in MIF schemas, tools and specifications or simply with the domain experts. Modeling core HL7 concepts, constraints, and semantic relationships in Ecore/EMF encapsulates the domain-specific knowledge in a transparent way while unifying Java, XML, and UML in an abstract, high-level representation. Moreover, persisting and versioning the core HL7 concepts as a single Ecore context allows modelers and implementers to create, edit and validate message models against a single modeling context. The solution discussed in this paper is implemented in the new HL7 Static Model Designer as an extensible toolset integrated as a standalone Eclipse RCP application. Please see our website http://b2i.sg for further information.]]>
One of the main challenges of achieving interoperability using the HL7 V3 healthcare standard is the lack of clear definition and supporting tools for modeling, testing, and conformance checking. Currently, the knowledge defining the modeling is scattered around in MIF schemas, tools and specifications or simply with the domain experts. Modeling core HL7 concepts, constraints, and semantic relationships in Ecore/EMF encapsulates the domain-specific knowledge in a transparent way while unifying Java, XML, and UML in an abstract, high-level representation. Moreover, persisting and versioning the core HL7 concepts as a single Ecore context allows modelers and implementers to create, edit and validate message models against a single modeling context. The solution discussed in this paper is implemented in the new HL7 Static Model Designer as an extensible toolset integrated as a standalone Eclipse RCP application. Please see our website http://b2i.sg for further information.]]> Fri, 05 Dec 2014 07:13:23 GMT /slideshow/implementing-an-hl7version3modelingtoolfromanecoremodel/42391637 Snow_Owl@slideshare.net(Snow_Owl) Implementing an HL7 version 3 modeling tool from an Ecore model Snow_Owl One of the main challenges of achieving interoperability using the HL7 V3 healthcare standard is the lack of clear definition and supporting tools for modeling, testing, and conformance checking. Currently, the knowledge defining the modeling is scattered around in MIF schemas, tools and specifications or simply with the domain experts. Modeling core HL7 concepts, constraints, and semantic relationships in Ecore/EMF encapsulates the domain-specific knowledge in a transparent way while unifying Java, XML, and UML in an abstract, high-level representation. Moreover, persisting and versioning the core HL7 concepts as a single Ecore context allows modelers and implementers to create, edit and validate message models against a single modeling context. The solution discussed in this paper is implemented in the new HL7 Static Model Designer as an extensible toolset integrated as a standalone Eclipse RCP application. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/implementinganhl7version3modelingtoolfromanecoremodel-141205071323-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> One of the main challenges of achieving interoperability using the HL7 V3 healthcare standard is the lack of clear definition and supporting tools for modeling, testing, and conformance checking. Currently, the knowledge defining the modeling is scattered around in MIF schemas, tools and specifications or simply with the domain experts. Modeling core HL7 concepts, constraints, and semantic relationships in Ecore/EMF encapsulates the domain-specific knowledge in a transparent way while unifying Java, XML, and UML in an abstract, high-level representation. Moreover, persisting and versioning the core HL7 concepts as a single Ecore context allows modelers and implementers to create, edit and validate message models against a single modeling context. The solution discussed in this paper is implemented in the new HL7 Static Model Designer as an extensible toolset integrated as a standalone Eclipse RCP application. Please see our website http://b2i.sg for further information.

Implementing an HL7 version 3 modeling tool from an Ecore model from Snow Owl

]]> 1233 4 https://cdn.slidesharecdn.com/ss_thumbnails/implementinganhl7version3modelingtoolfromanecoremodel-141205071323-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds document White http://activitystrea.ms/schema/1.0/post

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0 The Logical Model Designer - Binding Information Models to Terminology /slideshow/the-logical-model-designer-binding-information-models-to-terminology/42391514 ihtsdoshowcase2012logicalmodeldesigner-141205070934-conversion-gate01
This presentation demonstrates the functionality provided by the Logical Model Designer (LMD) and Snow Owl tools, which enables terminology to be bound to the Singapore Logical Information Model. Abstract: A critical enabler in the journey towards semantic interoperability in Singapore is the Singapore "˜Logical Information Model' (LIM). The LIM is a model of the healthcare information shared within Singapore, and is defined as a set of reusable "˜archetypes' for each clinical concept (e.g. Problem/Diagnosis, Pharmacy Order). These archetypes are then constrained and composed into "˜templates' to support specific use cases. The Singapore LIM harmonises the semantics of the information structures with the terminology, using multiple types of terminology bindings, including semantic, value domain and constraint bindings. Value domain bindings are defined to both national "˜reference terminology' (used for querying nationally-collated data), as well as to a variety of "˜interface terminologies' used within local clinical systems (required to enforce conformance-compliance rules over message specifications generated from the LIM). To support the diversity of pre-coordination captured in local interface terms, "˜design patterns' are included in the LIM, based on the SNOMED CT concept model. These design patterns represent a logical model of meaning for a specific concept, and allow more than one split between the information model and the terminology model to be represented in a semantically-consistent manner. This presentation will demonstrate the "˜Logical Model Designer' (LMD) - an Eclipse-based tool that is being used to maintain Singapore's Logical Information Model. A number of features of the LMD tooling will be demonstrated, with a specific focus on how the information structure is bound to the terminology via an interface to the Snow Owl platform. Value Domains are defined as reference sets within Snow Owl and then linked to the information structures defined in the LMD. Please see our website http://b2i.sg for further information.]]>
This presentation demonstrates the functionality provided by the Logical Model Designer (LMD) and Snow Owl tools, which enables terminology to be bound to the Singapore Logical Information Model. Abstract: A critical enabler in the journey towards semantic interoperability in Singapore is the Singapore "˜Logical Information Model' (LIM). The LIM is a model of the healthcare information shared within Singapore, and is defined as a set of reusable "˜archetypes' for each clinical concept (e.g. Problem/Diagnosis, Pharmacy Order). These archetypes are then constrained and composed into "˜templates' to support specific use cases. The Singapore LIM harmonises the semantics of the information structures with the terminology, using multiple types of terminology bindings, including semantic, value domain and constraint bindings. Value domain bindings are defined to both national "˜reference terminology' (used for querying nationally-collated data), as well as to a variety of "˜interface terminologies' used within local clinical systems (required to enforce conformance-compliance rules over message specifications generated from the LIM). To support the diversity of pre-coordination captured in local interface terms, "˜design patterns' are included in the LIM, based on the SNOMED CT concept model. These design patterns represent a logical model of meaning for a specific concept, and allow more than one split between the information model and the terminology model to be represented in a semantically-consistent manner. This presentation will demonstrate the "˜Logical Model Designer' (LMD) - an Eclipse-based tool that is being used to maintain Singapore's Logical Information Model. A number of features of the LMD tooling will be demonstrated, with a specific focus on how the information structure is bound to the terminology via an interface to the Snow Owl platform. Value Domains are defined as reference sets within Snow Owl and then linked to the information structures defined in the LMD. Please see our website http://b2i.sg for further information.]]> Fri, 05 Dec 2014 07:09:34 GMT /slideshow/the-logical-model-designer-binding-information-models-to-terminology/42391514 Snow_Owl@slideshare.net(Snow_Owl) The Logical Model Designer - Binding Information Models to Terminology Snow_Owl This presentation demonstrates the functionality provided by the Logical Model Designer (LMD) and Snow Owl tools, which enables terminology to be bound to the Singapore Logical Information Model. Abstract: A critical enabler in the journey towards semantic interoperability in Singapore is the Singapore "˜Logical Information Model' (LIM). The LIM is a model of the healthcare information shared within Singapore, and is defined as a set of reusable "˜archetypes' for each clinical concept (e.g. Problem/Diagnosis, Pharmacy Order). These archetypes are then constrained and composed into "˜templates' to support specific use cases. The Singapore LIM harmonises the semantics of the information structures with the terminology, using multiple types of terminology bindings, including semantic, value domain and constraint bindings. Value domain bindings are defined to both national "˜reference terminology' (used for querying nationally-collated data), as well as to a variety of "˜interface terminologies' used within local clinical systems (required to enforce conformance-compliance rules over message specifications generated from the LIM). To support the diversity of pre-coordination captured in local interface terms, "˜design patterns' are included in the LIM, based on the SNOMED CT concept model. These design patterns represent a logical model of meaning for a specific concept, and allow more than one split between the information model and the terminology model to be represented in a semantically-consistent manner. This presentation will demonstrate the "˜Logical Model Designer' (LMD) - an Eclipse-based tool that is being used to maintain Singapore's Logical Information Model. A number of features of the LMD tooling will be demonstrated, with a specific focus on how the information structure is bound to the terminology via an interface to the Snow Owl platform. Value Domains are defined as reference sets within Snow Owl and then linked to the information structures defined in the LMD. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ihtsdoshowcase2012logicalmodeldesigner-141205070934-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation demonstrates the functionality provided by the Logical Model Designer (LMD) and Snow Owl tools, which enables terminology to be bound to the Singapore Logical Information Model. Abstract: A critical enabler in the journey towards semantic interoperability in Singapore is the Singapore "˜Logical Information Model' (LIM). The LIM is a model of the healthcare information shared within Singapore, and is defined as a set of reusable "˜archetypes' for each clinical concept (e.g. Problem/Diagnosis, Pharmacy Order). These archetypes are then constrained and composed into "˜templates' to support specific use cases. The Singapore LIM harmonises the semantics of the information structures with the terminology, using multiple types of terminology bindings, including semantic, value domain and constraint bindings. Value domain bindings are defined to both national "˜reference terminology' (used for querying nationally-collated data), as well as to a variety of "˜interface terminologies' used within local clinical systems (required to enforce conformance-compliance rules over message specifications generated from the LIM). To support the diversity of pre-coordination captured in local interface terms, "˜design patterns' are included in the LIM, based on the SNOMED CT concept model. These design patterns represent a logical model of meaning for a specific concept, and allow more than one split between the information model and the terminology model to be represented in a semantically-consistent manner. This presentation will demonstrate the "˜Logical Model Designer' (LMD) - an Eclipse-based tool that is being used to maintain Singapore's Logical Information Model. A number of features of the LMD tooling will be demonstrated, with a specific focus on how the information structure is bound to the terminology via an interface to the Snow Owl platform. Value Domains are defined as reference sets within Snow Owl and then linked to the information structures defined in the LMD. Please see our website http://b2i.sg for further information.

The Logical Model Designer - Binding Information Models to Terminology from Snow Owl

]]> 2009 11 https://cdn.slidesharecdn.com/ss_thumbnails/ihtsdoshowcase2012logicalmodeldesigner-141205070934-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 Using Snow Owl to Maintain Singapore’s SNOMED CT Extension and Drug Dictionary /slideshow/using-snow-owl-to-maintain-singapores-snomed-ct-extension-and-drug-dictionary/42388096 snowowlsdd-141205051415-conversion-gate01
This presentation demonstrates the capabilities of the Snow Owl tool required by Singapore's National Release Centre, and to demonstrate the automatic generation of the Singapore Drug Dictionary ontology from a set of source drug definitions. Abstract: Snow Owl is a powerful platform, which enables terminologies to be browsed, searched, authored and validated. The Singapore National Release Centre is using Snow Owl to author, maintain, review and publish the Singapore national SNOMED CT extension, including the Singapore Drug Dictionary (SDD). The Singapore SNOMED CT extension includes Singapore preferred terms, extension concepts, relationships and descriptions, and a variety of reference sets, including a number of mappings. Each of these artefacts undergoes a quality-review process, enabled by Snow Owl's built-in task management module. Support for the creation and maintenance of the Singapore Drug Dictionary is implemented on top of the Snow Owl platform. Drug information is entered once using traditional data structures, which are linked to a series of SNOMED CT reference sets (e.g. "˜Dose Form', "˜Substance', "˜Container'). The drug information is then transformed into an ontology of concepts defined at different levels of abstraction, as required by each medication management use-case. The SDD reference set creation, review and publication processes are managed using Snow Owl's extensive set of features. Please see our website http://b2i.sg for further information.]]>
This presentation demonstrates the capabilities of the Snow Owl tool required by Singapore's National Release Centre, and to demonstrate the automatic generation of the Singapore Drug Dictionary ontology from a set of source drug definitions. Abstract: Snow Owl is a powerful platform, which enables terminologies to be browsed, searched, authored and validated. The Singapore National Release Centre is using Snow Owl to author, maintain, review and publish the Singapore national SNOMED CT extension, including the Singapore Drug Dictionary (SDD). The Singapore SNOMED CT extension includes Singapore preferred terms, extension concepts, relationships and descriptions, and a variety of reference sets, including a number of mappings. Each of these artefacts undergoes a quality-review process, enabled by Snow Owl's built-in task management module. Support for the creation and maintenance of the Singapore Drug Dictionary is implemented on top of the Snow Owl platform. Drug information is entered once using traditional data structures, which are linked to a series of SNOMED CT reference sets (e.g. "˜Dose Form', "˜Substance', "˜Container'). The drug information is then transformed into an ontology of concepts defined at different levels of abstraction, as required by each medication management use-case. The SDD reference set creation, review and publication processes are managed using Snow Owl's extensive set of features. Please see our website http://b2i.sg for further information.]]> Fri, 05 Dec 2014 05:14:15 GMT /slideshow/using-snow-owl-to-maintain-singapores-snomed-ct-extension-and-drug-dictionary/42388096 Snow_Owl@slideshare.net(Snow_Owl) Using Snow Owl to Maintain Singapore’s SNOMED CT Extension and Drug Dictionary Snow_Owl This presentation demonstrates the capabilities of the Snow Owl tool required by Singapore's National Release Centre, and to demonstrate the automatic generation of the Singapore Drug Dictionary ontology from a set of source drug definitions. Abstract: Snow Owl is a powerful platform, which enables terminologies to be browsed, searched, authored and validated. The Singapore National Release Centre is using Snow Owl to author, maintain, review and publish the Singapore national SNOMED CT extension, including the Singapore Drug Dictionary (SDD). The Singapore SNOMED CT extension includes Singapore preferred terms, extension concepts, relationships and descriptions, and a variety of reference sets, including a number of mappings. Each of these artefacts undergoes a quality-review process, enabled by Snow Owl's built-in task management module. Support for the creation and maintenance of the Singapore Drug Dictionary is implemented on top of the Snow Owl platform. Drug information is entered once using traditional data structures, which are linked to a series of SNOMED CT reference sets (e.g. "˜Dose Form', "˜Substance', "˜Container'). The drug information is then transformed into an ontology of concepts defined at different levels of abstraction, as required by each medication management use-case. The SDD reference set creation, review and publication processes are managed using Snow Owl's extensive set of features. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/snowowlsdd-141205051415-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation demonstrates the capabilities of the Snow Owl tool required by Singapore's National Release Centre, and to demonstrate the automatic generation of the Singapore Drug Dictionary ontology from a set of source drug definitions. Abstract: Snow Owl is a powerful platform, which enables terminologies to be browsed, searched, authored and validated. The Singapore National Release Centre is using Snow Owl to author, maintain, review and publish the Singapore national SNOMED CT extension, including the Singapore Drug Dictionary (SDD). The Singapore SNOMED CT extension includes Singapore preferred terms, extension concepts, relationships and descriptions, and a variety of reference sets, including a number of mappings. Each of these artefacts undergoes a quality-review process, enabled by Snow Owl's built-in task management module. Support for the creation and maintenance of the Singapore Drug Dictionary is implemented on top of the Snow Owl platform. Drug information is entered once using traditional data structures, which are linked to a series of SNOMED CT reference sets (e.g. "˜Dose Form', "˜Substance', "˜Container'). The drug information is then transformed into an ontology of concepts defined at different levels of abstraction, as required by each medication management use-case. The SDD reference set creation, review and publication processes are managed using Snow Owl's extensive set of features. Please see our website http://b2i.sg for further information.

Using Snow Owl to Maintain Singapore’s SNOMED CT Extension and Drug Dictionary from Snow Owl

]]> 2144 19 https://cdn.slidesharecdn.com/ss_thumbnails/snowowlsdd-141205051415-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 Searching SNOMED CT /slideshow/searching-snomed-ct/42388015 searchingsnomedct-screenshots-141205051106-conversion-gate02
The purpose of this presentation is to understand techniques for leveraging SNOMED CT's semantics in clinical document search and analysis. Abstract: The availability of semantically rich electronic health records utilizing SNOMED CT as a reference terminology continues to grow, providing new opportunities to improve patient care and reduce costs. However, traditional data warehouses struggle to unleash the full semantic meaning within the health records, as the data is built around a limited number of concepts. This presentation suggests an alternate strategy for executing meaningful queries. EHR data is represented using an information model bound to SNOMED CT terminology, where the information model is agnostic to the underlying standard (e.g. CIMI reference model, Singapores Logical Reference Model, the UKs Logical Record Architecture, HL7, openEHR). Meaningful queries can be formulated using a query language that utilizes the SNOMED CT compositional grammar for post-coordinated expressions. This allows querying on not only the concept hierarchy but also the defining relationships as well, resulting in semantically aggregated patient data. Complex queries can be executed in real-time for millions of EHRs without the need for extraction and aggregation to analytical stores. The results of the query can be further analysed using a cloud-based analytics engine. Please see our website http://b2i.sg for further information.]]>
The purpose of this presentation is to understand techniques for leveraging SNOMED CT's semantics in clinical document search and analysis. Abstract: The availability of semantically rich electronic health records utilizing SNOMED CT as a reference terminology continues to grow, providing new opportunities to improve patient care and reduce costs. However, traditional data warehouses struggle to unleash the full semantic meaning within the health records, as the data is built around a limited number of concepts. This presentation suggests an alternate strategy for executing meaningful queries. EHR data is represented using an information model bound to SNOMED CT terminology, where the information model is agnostic to the underlying standard (e.g. CIMI reference model, Singapores Logical Reference Model, the UKs Logical Record Architecture, HL7, openEHR). Meaningful queries can be formulated using a query language that utilizes the SNOMED CT compositional grammar for post-coordinated expressions. This allows querying on not only the concept hierarchy but also the defining relationships as well, resulting in semantically aggregated patient data. Complex queries can be executed in real-time for millions of EHRs without the need for extraction and aggregation to analytical stores. The results of the query can be further analysed using a cloud-based analytics engine. Please see our website http://b2i.sg for further information.]]> Fri, 05 Dec 2014 05:11:06 GMT /slideshow/searching-snomed-ct/42388015 Snow_Owl@slideshare.net(Snow_Owl) Searching SNOMED CT Snow_Owl The purpose of this presentation is to understand techniques for leveraging SNOMED CT's semantics in clinical document search and analysis. Abstract: The availability of semantically rich electronic health records utilizing SNOMED CT as a reference terminology continues to grow, providing new opportunities to improve patient care and reduce costs. However, traditional data warehouses struggle to unleash the full semantic meaning within the health records, as the data is built around a limited number of concepts. This presentation suggests an alternate strategy for executing meaningful queries. EHR data is represented using an information model bound to SNOMED CT terminology, where the information model is agnostic to the underlying standard (e.g. CIMI reference model, Singapores Logical Reference Model, the UKs Logical Record Architecture, HL7, openEHR). Meaningful queries can be formulated using a query language that utilizes the SNOMED CT compositional grammar for post-coordinated expressions. This allows querying on not only the concept hierarchy but also the defining relationships as well, resulting in semantically aggregated patient data. Complex queries can be executed in real-time for millions of EHRs without the need for extraction and aggregation to analytical stores. The results of the query can be further analysed using a cloud-based analytics engine. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/searchingsnomedct-screenshots-141205051106-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> The purpose of this presentation is to understand techniques for leveraging SNOMED CT's semantics in clinical document search and analysis. Abstract: The availability of semantically rich electronic health records utilizing SNOMED CT as a reference terminology continues to grow, providing new opportunities to improve patient care and reduce costs. However, traditional data warehouses struggle to unleash the full semantic meaning within the health records, as the data is built around a limited number of concepts. This presentation suggests an alternate strategy for executing meaningful queries. EHR data is represented using an information model bound to SNOMED CT terminology, where the information model is agnostic to the underlying standard (e.g. CIMI reference model, Singapores Logical Reference Model, the UKs Logical Record Architecture, HL7, openEHR). Meaningful queries can be formulated using a query language that utilizes the SNOMED CT compositional grammar for post-coordinated expressions. This allows querying on not only the concept hierarchy but also the defining relationships as well, resulting in semantically aggregated patient data. Complex queries can be executed in real-time for millions of EHRs without the need for extraction and aggregation to analytical stores. The results of the query can be further analysed using a cloud-based analytics engine. Please see our website http://b2i.sg for further information.

Searching SNOMED CT from Snow Owl

]]> 1518 2 https://cdn.slidesharecdn.com/ss_thumbnails/searchingsnomedct-screenshots-141205051106-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 Singapore Drug Dictionary - Developing and integrating a national drug extension with SNOMED CT /slideshow/singapore-drug-dictionary/42387030 sdd2013-141205043648-conversion-gate01
Maintaining a National Drug Dictionary sets both patient safety challenges and ontological difficulties to the National Release Centres. Pharmacists and ontologists have to cooperate to enable the integrated terminology management that is required to implement drug extensions. Snow Owl—a terminology management tool—has been extended with a profile for pharmacists for aided data entry utilizing Singapore extension SNOMED CT reference sets. An ontology generation process bridges between the raw pharmacy data and the ontological representation. The generated SNOMED CT concepts are fully defined and augmented with special description logic features to ensure that the classification returns valid results even in unusual use cases, like multi-ingredient products. The generated ontology includes all the semantics necessary to build intensional reference sets that support different clinical use cases like prescribing, dispensing or administration while the semantic query mechanisms that employ the ontological nature of SNOMED CT foster research and decision support on the Drug Dictionary. Please see our website http://b2i.sg for further information.]]>
Maintaining a National Drug Dictionary sets both patient safety challenges and ontological difficulties to the National Release Centres. Pharmacists and ontologists have to cooperate to enable the integrated terminology management that is required to implement drug extensions. Snow Owl—a terminology management tool—has been extended with a profile for pharmacists for aided data entry utilizing Singapore extension SNOMED CT reference sets. An ontology generation process bridges between the raw pharmacy data and the ontological representation. The generated SNOMED CT concepts are fully defined and augmented with special description logic features to ensure that the classification returns valid results even in unusual use cases, like multi-ingredient products. The generated ontology includes all the semantics necessary to build intensional reference sets that support different clinical use cases like prescribing, dispensing or administration while the semantic query mechanisms that employ the ontological nature of SNOMED CT foster research and decision support on the Drug Dictionary. Please see our website http://b2i.sg for further information.]]> Fri, 05 Dec 2014 04:36:48 GMT /slideshow/singapore-drug-dictionary/42387030 Snow_Owl@slideshare.net(Snow_Owl) Singapore Drug Dictionary - Developing and integrating a national drug extension with SNOMED CT Snow_Owl Maintaining a National Drug Dictionary sets both patient safety challenges and ontological difficulties to the National Release Centres. Pharmacists and ontologists have to cooperate to enable the integrated terminology management that is required to implement drug extensions. Snow Owl—a terminology management tool—has been extended with a profile for pharmacists for aided data entry utilizing Singapore extension SNOMED CT reference sets. An ontology generation process bridges between the raw pharmacy data and the ontological representation. The generated SNOMED CT concepts are fully defined and augmented with special description logic features to ensure that the classification returns valid results even in unusual use cases, like multi-ingredient products. The generated ontology includes all the semantics necessary to build intensional reference sets that support different clinical use cases like prescribing, dispensing or administration while the semantic query mechanisms that employ the ontological nature of SNOMED CT foster research and decision support on the Drug Dictionary. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sdd2013-141205043648-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Maintaining a National Drug Dictionary sets both patient safety challenges and ontological difficulties to the National Release Centres. Pharmacists and ontologists have to cooperate to enable the integrated terminology management that is required to implement drug extensions. Snow Owl—a terminology management tool—has been extended with a profile for pharmacists for aided data entry utilizing Singapore extension SNOMED CT reference sets. An ontology generation process bridges between the raw pharmacy data and the ontological representation. The generated SNOMED CT concepts are fully defined and augmented with special description logic features to ensure that the classification returns valid results even in unusual use cases, like multi-ingredient products. The generated ontology includes all the semantics necessary to build intensional reference sets that support different clinical use cases like prescribing, dispensing or administration while the semantic query mechanisms that employ the ontological nature of SNOMED CT foster research and decision support on the Drug Dictionary. Please see our website http://b2i.sg for further information.

Singapore Drug Dictionary - Developing and integrating a national drug extension with SNOMED CT from Snow Owl

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0 Revision-controlled collaborative terminology authoring /slideshow/revisioncontrolled-collaborative-terminology-authoring/42355746 soversioning-141204093404-conversion-gate02
The concepts of collaborative development aided by revision control systems have been well known in the software industry for decades. Some of these systems have been adopted in terminology authoring tools like the IHTSDO Workbench to support collaborative authoring workflows. This talk discusses implementation challenges and lessons learned when applying collaborative development techniques to terminology authoring. Representative terminology authoring use cases from the Singaporean, Australian, and Canadian National Release Centers will be discussed in terms of their impact on revisioning requirements. A concrete scenario for SNOMED CT will be demonstrated including examples for versioning, comparing versions and patching an older version. The scenario will be driven through a representative collaborative workflow. Useful open-source components will be discussed along with practical experiences in integrating them with Snow Owl, a commercial terminology authoring application. Revision control features including change history, comparison, versioning and patching of the terminologies will be discussed and compared to alternate approaches. The impact of workflow to drive the process will be illustrated using the tool. Optimizations and performance challenges will also be briefly covered. Please see our website http://b2i.sg for further information.]]>
The concepts of collaborative development aided by revision control systems have been well known in the software industry for decades. Some of these systems have been adopted in terminology authoring tools like the IHTSDO Workbench to support collaborative authoring workflows. This talk discusses implementation challenges and lessons learned when applying collaborative development techniques to terminology authoring. Representative terminology authoring use cases from the Singaporean, Australian, and Canadian National Release Centers will be discussed in terms of their impact on revisioning requirements. A concrete scenario for SNOMED CT will be demonstrated including examples for versioning, comparing versions and patching an older version. The scenario will be driven through a representative collaborative workflow. Useful open-source components will be discussed along with practical experiences in integrating them with Snow Owl, a commercial terminology authoring application. Revision control features including change history, comparison, versioning and patching of the terminologies will be discussed and compared to alternate approaches. The impact of workflow to drive the process will be illustrated using the tool. Optimizations and performance challenges will also be briefly covered. Please see our website http://b2i.sg for further information.]]> Thu, 04 Dec 2014 09:34:04 GMT /slideshow/revisioncontrolled-collaborative-terminology-authoring/42355746 Snow_Owl@slideshare.net(Snow_Owl) Revision-controlled collaborative terminology authoring Snow_Owl The concepts of collaborative development aided by revision control systems have been well known in the software industry for decades. Some of these systems have been adopted in terminology authoring tools like the IHTSDO Workbench to support collaborative authoring workflows. This talk discusses implementation challenges and lessons learned when applying collaborative development techniques to terminology authoring. Representative terminology authoring use cases from the Singaporean, Australian, and Canadian National Release Centers will be discussed in terms of their impact on revisioning requirements. A concrete scenario for SNOMED CT will be demonstrated including examples for versioning, comparing versions and patching an older version. The scenario will be driven through a representative collaborative workflow. Useful open-source components will be discussed along with practical experiences in integrating them with Snow Owl, a commercial terminology authoring application. Revision control features including change history, comparison, versioning and patching of the terminologies will be discussed and compared to alternate approaches. The impact of workflow to drive the process will be illustrated using the tool. Optimizations and performance challenges will also be briefly covered. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/soversioning-141204093404-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> The concepts of collaborative development aided by revision control systems have been well known in the software industry for decades. Some of these systems have been adopted in terminology authoring tools like the IHTSDO Workbench to support collaborative authoring workflows. This talk discusses implementation challenges and lessons learned when applying collaborative development techniques to terminology authoring. Representative terminology authoring use cases from the Singaporean, Australian, and Canadian National Release Centers will be discussed in terms of their impact on revisioning requirements. A concrete scenario for SNOMED CT will be demonstrated including examples for versioning, comparing versions and patching an older version. The scenario will be driven through a representative collaborative workflow. Useful open-source components will be discussed along with practical experiences in integrating them with Snow Owl, a commercial terminology authoring application. Revision control features including change history, comparison, versioning and patching of the terminologies will be discussed and compared to alternate approaches. The impact of workflow to drive the process will be illustrated using the tool. Optimizations and performance challenges will also be briefly covered. Please see our website http://b2i.sg for further information.

Revision-controlled collaborative terminology authoring from Snow Owl

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0 A simple web-based interface for advanced SNOMED CT queries /slideshow/mq-search/42354673 mqsearch-141204091043-conversion-gate01
SNOMED CT – as the most comprehensive biomedical ontology – has the potential to utilize semantic query methods that operate on the defining attributes of the concepts. This type of semantic querying is widely used, and some of the query languages already extended the attribute constraints with the option for limited lexical and metadata search criteria. Since the introduction of RF2 the expressibility of SNOMED CT can increase, and various national extensions make use of this extensibility by adding specific description logic features that are relevant for their content. An example for this is the Singapore Drug Dictionary that is based on the SNOMED CT concept model, but applies additional attribute types. The standard query languages are not powerful enough for such content. This demonstration introduces a search interface that allows querying both standard SNOMED CT content as well as pharmaceutical extensions that utilize optional description logic extensions. These advanced queries are created by terminologists with an understanding of SNOMED CT. End-users can then use these queries to browse relevant subsets of the terminology appropriate for their use case. For example, clinicians can browse only drugs that are clinically relevant, while regulators can constrain their searches to controlled substances. The tool also allows early validation of intensional reference set content, without having to implement and publish the reference sets. Practical examples using an online browser (Snow Owl Web) will highlight challenges and lessons learnt when working with real-world clinicians and regulators lacking SNOMED CT training. Please see our website http://b2i.sg for further information.]]>
SNOMED CT – as the most comprehensive biomedical ontology – has the potential to utilize semantic query methods that operate on the defining attributes of the concepts. This type of semantic querying is widely used, and some of the query languages already extended the attribute constraints with the option for limited lexical and metadata search criteria. Since the introduction of RF2 the expressibility of SNOMED CT can increase, and various national extensions make use of this extensibility by adding specific description logic features that are relevant for their content. An example for this is the Singapore Drug Dictionary that is based on the SNOMED CT concept model, but applies additional attribute types. The standard query languages are not powerful enough for such content. This demonstration introduces a search interface that allows querying both standard SNOMED CT content as well as pharmaceutical extensions that utilize optional description logic extensions. These advanced queries are created by terminologists with an understanding of SNOMED CT. End-users can then use these queries to browse relevant subsets of the terminology appropriate for their use case. For example, clinicians can browse only drugs that are clinically relevant, while regulators can constrain their searches to controlled substances. The tool also allows early validation of intensional reference set content, without having to implement and publish the reference sets. Practical examples using an online browser (Snow Owl Web) will highlight challenges and lessons learnt when working with real-world clinicians and regulators lacking SNOMED CT training. Please see our website http://b2i.sg for further information.]]> Thu, 04 Dec 2014 09:10:43 GMT /slideshow/mq-search/42354673 Snow_Owl@slideshare.net(Snow_Owl) A simple web-based interface for advanced SNOMED CT queries Snow_Owl SNOMED CT – as the most comprehensive biomedical ontology – has the potential to utilize semantic query methods that operate on the defining attributes of the concepts. This type of semantic querying is widely used, and some of the query languages already extended the attribute constraints with the option for limited lexical and metadata search criteria. Since the introduction of RF2 the expressibility of SNOMED CT can increase, and various national extensions make use of this extensibility by adding specific description logic features that are relevant for their content. An example for this is the Singapore Drug Dictionary that is based on the SNOMED CT concept model, but applies additional attribute types. The standard query languages are not powerful enough for such content. This demonstration introduces a search interface that allows querying both standard SNOMED CT content as well as pharmaceutical extensions that utilize optional description logic extensions. These advanced queries are created by terminologists with an understanding of SNOMED CT. End-users can then use these queries to browse relevant subsets of the terminology appropriate for their use case. For example, clinicians can browse only drugs that are clinically relevant, while regulators can constrain their searches to controlled substances. The tool also allows early validation of intensional reference set content, without having to implement and publish the reference sets. Practical examples using an online browser (Snow Owl Web) will highlight challenges and lessons learnt when working with real-world clinicians and regulators lacking SNOMED CT training. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mqsearch-141204091043-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> SNOMED CT – as the most comprehensive biomedical ontology – has the potential to utilize semantic query methods that operate on the defining attributes of the concepts. This type of semantic querying is widely used, and some of the query languages already extended the attribute constraints with the option for limited lexical and metadata search criteria. Since the introduction of RF2 the expressibility of SNOMED CT can increase, and various national extensions make use of this extensibility by adding specific description logic features that are relevant for their content. An example for this is the Singapore Drug Dictionary that is based on the SNOMED CT concept model, but applies additional attribute types. The standard query languages are not powerful enough for such content. This demonstration introduces a search interface that allows querying both standard SNOMED CT content as well as pharmaceutical extensions that utilize optional description logic extensions. These advanced queries are created by terminologists with an understanding of SNOMED CT. End-users can then use these queries to browse relevant subsets of the terminology appropriate for their use case. For example, clinicians can browse only drugs that are clinically relevant, while regulators can constrain their searches to controlled substances. The tool also allows early validation of intensional reference set content, without having to implement and publish the reference sets. Practical examples using an online browser (Snow Owl Web) will highlight challenges and lessons learnt when working with real-world clinicians and regulators lacking SNOMED CT training. Please see our website http://b2i.sg for further information.

A simple web-based interface for advanced SNOMED CT queries from Snow Owl

]]> 2008 2 https://cdn.slidesharecdn.com/ss_thumbnails/mqsearch-141204091043-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 Introduction to Snow Owl - A tool for SNOMED CT /slideshow/introduction-to-snow-owl/42353204 introductiontosnowowl-141204083506-conversion-gate02
This tutorial gives an overview of the main functions of Snow Owl, which is a tool for browsing and authoring clinical terminologies (e.g. SNOMED CT, ICD-10, ICD-10-AM, ATC, LOINC). The following topics are covered: Introduction to the user interface, browsing, content authoring, reference sets, and semantic searches. Exercises at the end of each section allow you to test your knowledge. The slides are from the Snow Owl workshop at the 2013 IHTSDO showcase in Washington, D.C. Please see our website http://b2i.sg for further information.]]>
This tutorial gives an overview of the main functions of Snow Owl, which is a tool for browsing and authoring clinical terminologies (e.g. SNOMED CT, ICD-10, ICD-10-AM, ATC, LOINC). The following topics are covered: Introduction to the user interface, browsing, content authoring, reference sets, and semantic searches. Exercises at the end of each section allow you to test your knowledge. The slides are from the Snow Owl workshop at the 2013 IHTSDO showcase in Washington, D.C. Please see our website http://b2i.sg for further information.]]> Thu, 04 Dec 2014 08:35:05 GMT /slideshow/introduction-to-snow-owl/42353204 Snow_Owl@slideshare.net(Snow_Owl) Introduction to Snow Owl - A tool for SNOMED CT Snow_Owl This tutorial gives an overview of the main functions of Snow Owl, which is a tool for browsing and authoring clinical terminologies (e.g. SNOMED CT, ICD-10, ICD-10-AM, ATC, LOINC). The following topics are covered: Introduction to the user interface, browsing, content authoring, reference sets, and semantic searches. Exercises at the end of each section allow you to test your knowledge. The slides are from the Snow Owl workshop at the 2013 IHTSDO showcase in Washington, D.C. Please see our website http://b2i.sg for further information. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontosnowowl-141204083506-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This tutorial gives an overview of the main functions of Snow Owl, which is a tool for browsing and authoring clinical terminologies (e.g. SNOMED CT, ICD-10, ICD-10-AM, ATC, LOINC). The following topics are covered: Introduction to the user interface, browsing, content authoring, reference sets, and semantic searches. Exercises at the end of each section allow you to test your knowledge. The slides are from the Snow Owl workshop at the 2013 IHTSDO showcase in Washington, D.C. Please see our website http://b2i.sg for further information.

Introduction to Snow Owl - A tool for SNOMED CT from Snow Owl

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0 https://cdn.slidesharecdn.com/profile-photo-Snow_Owl-48x48.jpg?cb=1708614510 B2i Healthcare is a boutique software engineering firm specialized in SNOMED CT® and healthcare information standards and exchange. We provide products to simplify SNOMED CT adoption and offer software development services to support your healthcare IT needs.Our Snow Owl technology family is deployed in over 2,500 locations in 83+ countries worldwide. Snow Owl is the most complete and trusted terminology authoring tool on the market. b2i.sg https://cdn.slidesharecdn.com/ss_thumbnails/snowowlintro-141205083415-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/snow-owl-platform-unlocking-the-meaning-of-healthcare-data/42394433 Snow Owl Platform. Unl... https://cdn.slidesharecdn.com/ss_thumbnails/implementingreusablesoftwarecomponentsmie2014-141205073336-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/implementing-reusable-softwarecomponentsmie2014/42392257 Implementing reusable ... https://cdn.slidesharecdn.com/ss_thumbnails/implementinganhl7version3modelingtoolfromanecoremodel-141205071323-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/implementing-an-hl7version3modelingtoolfromanecoremodel/42391637 Implementing an HL7 ve...