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.
SNOMED CT is a clinical terminology used for coding, retrieving, and analyzing health care data. It consists of codes, terms, and relationships that can precisely record and represent clinical information across health care. SNOMED CT concepts are organized into hierarchies and linked through relationships. It aims to enable automated clinical decision support and research by structuring information in a semantically meaningful way.
Anne Casey RN MSc FRCN
Editor, Paediatric Nursing
Royal College of Nursing Adviser on Information Standards
Clinical Domain Lead, NHS Information Standards Board for Health and Social Care
(15/10/08, SNOMED Workshop)
Introduction to medical coding standards and SNOMED-CTFarzad Jahedi
This document provides an introduction to coding medical data using SNOMED-CT. It discusses reasons for storing medical data digitally such as for patient care, quality control, and research. Coding data provides benefits like data reduction, standardized terminology, enabling statistics and research, and supporting management. The document then covers various clinical coding systems and classifications like ICD, SNOMED-CT, and DSM. It describes how SNOMED-CT organizes concepts, descriptions, relationships, and codes to represent medical meanings and allows mapping between clinical terminologies like ICD-9 and SNOMED-CT.
SNOMED CT is a clinical terminology system that allows clinicians to precisely document clinical thoughts. It consists of over 1 million medical terms and concepts related to health and healthcare. SNOMED CT supports natural language use by clinicians and precise documentation of clinical observations for purposes like clinical decision support and healthcare analytics. The terminology can represent clinical thoughts through both pre-coordinated and post-coordinated expressions while keeping the machine-readable expressions hidden from users.
SNOMED-CT is a standardized clinical terminology system that enables computers to understand medical concepts and their relationships. It consists of over a million concept codes along with descriptions, relationships between concepts, and other components. Concepts are linked by defined relationships, such as "is a" and causative relationships, to describe clinical meanings and aid in indexing, retrieving, and exchanging clinical data to support clinical decision-making.
This presentation deals with the basics of SNOMED CT with respect to it being a code for computer systems to interpret medical knowledge and initiate action. This is explained specifically with the medical professionals in mind.
It begins by discussing what SNOMED CT actually is and then moving on to demonstrate how the code system can be used to merge clinical documents written in different languages into one as well as how it can help in automating repetitive tasks using an if-then-else rules engine.
Snomed ct worked example dental interface terminologySB BHATTACHARYYA
This document discusses how dental procedures are represented in SNOMED CT using the example of extracting the permanent lower left first molar tooth (tooth 36). It explains that SNOMED CT represents this procedure as a postcoordinated expression combining the extracting permanent tooth procedure with the specific tooth site. While dental systems may refer to the procedure as extraction of tooth 36, there is no single concept for this exact term in SNOMED CT. The document outlines how the interface terminology used in dental systems would need to be mapped to corresponding SNOMED CT postcoordinated expressions.
SNOMED CT is a clinical terminology used for coding, retrieving, and analyzing health care data. It consists of codes, terms, and relationships that can precisely record and represent clinical information across health care. SNOMED CT concepts are organized into hierarchies and linked through relationships. It aims to enable automated clinical decision support and research by structuring information in a semantically meaningful way.
Anne Casey RN MSc FRCN
Editor, Paediatric Nursing
Royal College of Nursing Adviser on Information Standards
Clinical Domain Lead, NHS Information Standards Board for Health and Social Care
(15/10/08, SNOMED Workshop)
Introduction to medical coding standards and SNOMED-CTFarzad Jahedi
This document provides an introduction to coding medical data using SNOMED-CT. It discusses reasons for storing medical data digitally such as for patient care, quality control, and research. Coding data provides benefits like data reduction, standardized terminology, enabling statistics and research, and supporting management. The document then covers various clinical coding systems and classifications like ICD, SNOMED-CT, and DSM. It describes how SNOMED-CT organizes concepts, descriptions, relationships, and codes to represent medical meanings and allows mapping between clinical terminologies like ICD-9 and SNOMED-CT.
SNOMED CT is a clinical terminology system that allows clinicians to precisely document clinical thoughts. It consists of over 1 million medical terms and concepts related to health and healthcare. SNOMED CT supports natural language use by clinicians and precise documentation of clinical observations for purposes like clinical decision support and healthcare analytics. The terminology can represent clinical thoughts through both pre-coordinated and post-coordinated expressions while keeping the machine-readable expressions hidden from users.
SNOMED-CT is a standardized clinical terminology system that enables computers to understand medical concepts and their relationships. It consists of over a million concept codes along with descriptions, relationships between concepts, and other components. Concepts are linked by defined relationships, such as "is a" and causative relationships, to describe clinical meanings and aid in indexing, retrieving, and exchanging clinical data to support clinical decision-making.
This presentation deals with the basics of SNOMED CT with respect to it being a code for computer systems to interpret medical knowledge and initiate action. This is explained specifically with the medical professionals in mind.
It begins by discussing what SNOMED CT actually is and then moving on to demonstrate how the code system can be used to merge clinical documents written in different languages into one as well as how it can help in automating repetitive tasks using an if-then-else rules engine.
Snomed ct worked example dental interface terminologySB BHATTACHARYYA
This document discusses how dental procedures are represented in SNOMED CT using the example of extracting the permanent lower left first molar tooth (tooth 36). It explains that SNOMED CT represents this procedure as a postcoordinated expression combining the extracting permanent tooth procedure with the specific tooth site. While dental systems may refer to the procedure as extraction of tooth 36, there is no single concept for this exact term in SNOMED CT. The document outlines how the interface terminology used in dental systems would need to be mapped to corresponding SNOMED CT postcoordinated expressions.
Clinical models can be defined as reusable representations of clinical concepts that express relevant data for any given situation. They include detailed clinical models, openEHR archetypes, and templates. Archetypes define atomic health concepts and aim to express all relevant data for recording that concept. Templates are use case specific constraints and aggregations of archetypes used to create clinical specifications. Together, archetypes and templates provide a standardized yet flexible approach to representing clinical information.
This document discusses automating the handling of clinical terms using SNOMED CT description logic. It presents an algorithm that accepts clinical terms from users and processes them behind the scenes into machine-readable SNOMED CT codes. These codes are then analyzed to derive relationships and attributes, and presented back to users in human-readable form. Screenshots demonstrate the ability to process different clinical data types such as findings, diseases, and investigations. The goals are to handle clinical data in coded form behind the scenes while accepting and presenting data to users in plain language.
This document provides an overview and comparison of the MDConsult and FirstConsult mobile medical reference applications. MDConsult allows users to browse or search predefined topics and includes links to full text medical articles and books. FirstConsult is a downloaded app that provides summarized information on predefined conditions without requiring online access. Both resources give point-of-care clinical information but FirstConsult content is optimized for mobile and offline use.
This document discusses using formal modeling techniques like openEHR to improve the maintainability of clinical software. It summarizes research modeling the Minimal Standard Terminology for Digestive Endoscopy (MST) using openEHR archetypes. Implementing change requests from a previous endoscopy application in both the original application and a new one based on openEHR models found the openEHR-based application was significantly easier to maintain. Formal modeling addresses issues with non-standard clinical language and supports semantic interoperability and multilingual requirements.
Understand what ICD-10 is all about, what it looks like, and how it will affect you and your team. Learn how to create a focused and organized strategic ICD-10 plan
Evaluate and enhance clinical documentation to capture greater detail. Set up and establish documentation agreement with code factors. Get an important timeline to follow so you’re prepped and ready.
A brief introduction to SNOMED CT - the ontology based medical terminology. This covers the basic definitions, the difference between SNOMED CT and ICD9, Post co-ordination use-cases and some general information.
This is not an extensive guide for SNOMED CT adoption in a system
III Edició "The British Experience in Technologies for Health". Hospital de Sant Pau, Barcelona. 9 de novembre de 2011. Esdeveniment organitzat per la Fundació TICSalut i el Departament de Comerç i Inversions del Consolat General Britànic a Barcelona, UK Trade & Investment, per posar en contacte oportunitats i coneixements entre el Regne Unit i Catalunya.
Interlinking Online Communities and Enriching Social Software with the Semant...John Breslin
This document summarizes a presentation about interlinking online communities using Semantic Web technologies. It discusses:
1. The SIOC (Semantically-Interlinked Online Communities) project which aims to semantically connect online discussion sites through a common data model.
2. How SIOC represents the structure and content of communities using RDF properties and classes. Communities can then exchange and query data using common semantics.
3. Tools that export community data into RDF using SIOC, including for WordPress, vBulletin, and phpBB. This allows interlinking users, content, and activities across sites.
The document discusses the use of SNOMED-CT and ICD codes in electronic health records to meet meaningful use criteria. It states that SNOMED-CT allows providers to communicate using a common language and improves accuracy of patient data analysis. It also notes that healthcare providers must be aware of and use SNOMED-CT in their EHRs to meet meaningful use incentives and support the transition from ICD-9 to ICD-10 coding.
Implementing reusable software components for SNOMED CT diagram and expressio...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.
Linkages to EHRs and Related Standards. What can we learn from the Parallel U...Koray Atalag
This is the prezo I used during the CellML workshop in Waiheke Island, Auckland, New Zealand on 13 April 2015. The aim was to introduce information modelling methods and tools for the purpose of inspiring computational modelling work in the area of semantics and interoperability.
Pablo Pazos Gutiérrez gave a talk on developing openEHR systems. He discussed storing openEHR data using different database types, openEHR system architectures that have evolved to be more distributed and service-oriented, generating user interfaces from archetypes and templates, performing archetype-based validation on entered data, querying and visualizing openEHR data, and implementing openEHR over the past 8 years in Latin America.
The document discusses issues biomedical projects face when accessing clinical datasets due to disparate data formats. It presents a proposed solution of annotating clinical datasets with openEHR Archetypes, which are standards-based models of clinical concepts, to enable computer-based discovery of clinical information. The proposed technique involves transforming Archetypes into an "ontology of reality" by identifying clinical concepts and terminology codes to annotate datasets. This would allow complete clinical concepts, rather than just attributes, to be annotated and discovered from datasets.
SNOMED CT is a comprehensive clinical healthcare terminology that enables consistent representation of clinical data. It has the following key features:
1) It is the most comprehensive clinical terminology in the world, containing over 300,000 concepts and 1.5 million descriptions and relationships.
2) SNOMED CT concepts are organized into hierarchies and relationships that provide a structured framework for clinical meaning.
3) By implementing standardized clinical terminology and structure, SNOMED CT allows for improved data exchange and reuse, decision support, and analytics across systems and countries.
Bringing Things Together and Linking to Health Information using openEHRKoray Atalag
My prezo at Medinfo 2015 Conference in the workshop:
Digital Patient Modeling and Clinical Decision Support by Kerstin Denecke, Stefan Kropf, Claire Chalopin, Mario A, Cypko, Yihan Deng, Jan Gaebel, Koray Atalag
Design and implementation of Clinical Databases using openEHRPablo Pazos
This document provides an overview of designing and implementing clinical databases using openEHR. It discusses clinical information requirements, organization, and database technologies. OpenEHR's goals are to create flexible, interoperable EHRs through archetypes and templates that define clinical concepts. For database design, archetype IDs, paths, and node IDs are important for querying openEHR data. Relational databases can be used through object-relational mapping, mapping classes to tables, relationships, and inheritance.
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Presentation given at OSCON 2009 and PostgreSQL West 09. Describes SQL solutions to a selection of object-oriented problems:
- Extensibility
- Polymorphism
- Hierarchies
- Using ORM in MVC application architecture
These slides are excerpted from another presentation, "SQL Antipatterns Strike Back."
Clinical models can be defined as reusable representations of clinical concepts that express relevant data for any given situation. They include detailed clinical models, openEHR archetypes, and templates. Archetypes define atomic health concepts and aim to express all relevant data for recording that concept. Templates are use case specific constraints and aggregations of archetypes used to create clinical specifications. Together, archetypes and templates provide a standardized yet flexible approach to representing clinical information.
This document discusses automating the handling of clinical terms using SNOMED CT description logic. It presents an algorithm that accepts clinical terms from users and processes them behind the scenes into machine-readable SNOMED CT codes. These codes are then analyzed to derive relationships and attributes, and presented back to users in human-readable form. Screenshots demonstrate the ability to process different clinical data types such as findings, diseases, and investigations. The goals are to handle clinical data in coded form behind the scenes while accepting and presenting data to users in plain language.
This document provides an overview and comparison of the MDConsult and FirstConsult mobile medical reference applications. MDConsult allows users to browse or search predefined topics and includes links to full text medical articles and books. FirstConsult is a downloaded app that provides summarized information on predefined conditions without requiring online access. Both resources give point-of-care clinical information but FirstConsult content is optimized for mobile and offline use.
This document discusses using formal modeling techniques like openEHR to improve the maintainability of clinical software. It summarizes research modeling the Minimal Standard Terminology for Digestive Endoscopy (MST) using openEHR archetypes. Implementing change requests from a previous endoscopy application in both the original application and a new one based on openEHR models found the openEHR-based application was significantly easier to maintain. Formal modeling addresses issues with non-standard clinical language and supports semantic interoperability and multilingual requirements.
Understand what ICD-10 is all about, what it looks like, and how it will affect you and your team. Learn how to create a focused and organized strategic ICD-10 plan
Evaluate and enhance clinical documentation to capture greater detail. Set up and establish documentation agreement with code factors. Get an important timeline to follow so you’re prepped and ready.
A brief introduction to SNOMED CT - the ontology based medical terminology. This covers the basic definitions, the difference between SNOMED CT and ICD9, Post co-ordination use-cases and some general information.
This is not an extensive guide for SNOMED CT adoption in a system
III Edició "The British Experience in Technologies for Health". Hospital de Sant Pau, Barcelona. 9 de novembre de 2011. Esdeveniment organitzat per la Fundació TICSalut i el Departament de Comerç i Inversions del Consolat General Britànic a Barcelona, UK Trade & Investment, per posar en contacte oportunitats i coneixements entre el Regne Unit i Catalunya.
Interlinking Online Communities and Enriching Social Software with the Semant...John Breslin
This document summarizes a presentation about interlinking online communities using Semantic Web technologies. It discusses:
1. The SIOC (Semantically-Interlinked Online Communities) project which aims to semantically connect online discussion sites through a common data model.
2. How SIOC represents the structure and content of communities using RDF properties and classes. Communities can then exchange and query data using common semantics.
3. Tools that export community data into RDF using SIOC, including for WordPress, vBulletin, and phpBB. This allows interlinking users, content, and activities across sites.
The document discusses the use of SNOMED-CT and ICD codes in electronic health records to meet meaningful use criteria. It states that SNOMED-CT allows providers to communicate using a common language and improves accuracy of patient data analysis. It also notes that healthcare providers must be aware of and use SNOMED-CT in their EHRs to meet meaningful use incentives and support the transition from ICD-9 to ICD-10 coding.
Implementing reusable software components for SNOMED CT diagram and expressio...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.
Linkages to EHRs and Related Standards. What can we learn from the Parallel U...Koray Atalag
This is the prezo I used during the CellML workshop in Waiheke Island, Auckland, New Zealand on 13 April 2015. The aim was to introduce information modelling methods and tools for the purpose of inspiring computational modelling work in the area of semantics and interoperability.
Pablo Pazos Gutiérrez gave a talk on developing openEHR systems. He discussed storing openEHR data using different database types, openEHR system architectures that have evolved to be more distributed and service-oriented, generating user interfaces from archetypes and templates, performing archetype-based validation on entered data, querying and visualizing openEHR data, and implementing openEHR over the past 8 years in Latin America.
The document discusses issues biomedical projects face when accessing clinical datasets due to disparate data formats. It presents a proposed solution of annotating clinical datasets with openEHR Archetypes, which are standards-based models of clinical concepts, to enable computer-based discovery of clinical information. The proposed technique involves transforming Archetypes into an "ontology of reality" by identifying clinical concepts and terminology codes to annotate datasets. This would allow complete clinical concepts, rather than just attributes, to be annotated and discovered from datasets.
SNOMED CT is a comprehensive clinical healthcare terminology that enables consistent representation of clinical data. It has the following key features:
1) It is the most comprehensive clinical terminology in the world, containing over 300,000 concepts and 1.5 million descriptions and relationships.
2) SNOMED CT concepts are organized into hierarchies and relationships that provide a structured framework for clinical meaning.
3) By implementing standardized clinical terminology and structure, SNOMED CT allows for improved data exchange and reuse, decision support, and analytics across systems and countries.
Bringing Things Together and Linking to Health Information using openEHRKoray Atalag
My prezo at Medinfo 2015 Conference in the workshop:
Digital Patient Modeling and Clinical Decision Support by Kerstin Denecke, Stefan Kropf, Claire Chalopin, Mario A, Cypko, Yihan Deng, Jan Gaebel, Koray Atalag
Design and implementation of Clinical Databases using openEHRPablo Pazos
This document provides an overview of designing and implementing clinical databases using openEHR. It discusses clinical information requirements, organization, and database technologies. OpenEHR's goals are to create flexible, interoperable EHRs through archetypes and templates that define clinical concepts. For database design, archetype IDs, paths, and node IDs are important for querying openEHR data. Relational databases can be used through object-relational mapping, mapping classes to tables, relationships, and inheritance.
Enabling Clinical Data Reuse with openEHR Data Warehouse EnvironmentsLuis Marco Ruiz
Databases for Clinical Information Systems are difficult to
design and implement, especially when the design should be
compliant with a formal specification or standard. The
openEHR specifications offer a very expressive and generic
model for clinical data structures, allowing semantic
interoperability and compatibility with other standards like
HL7 CDA, FHIR, and ASTM CCR. But openEHR is not only
for data modeling, it specifies an EHR Computational
Platform designed to create highly modifiable future-proof
EHR systems, and to support long term economically viable
projects, with a knowledge-oriented approach that is
independent from specific technologies. Software Developers
find a great complexity in designing openEHR compliant
databases since the specifications do not include any
guidelines in that area. The authors of this tutorial are
developers that had to overcome these challenges. This
tutorial will expose different requirements, design principles,
technologies, techniques and main challenges of implementing
an openEHR-based Clinical Database, with examples and
lessons learned to help designers and developers to overcome the challenges more easily
Presentation given at OSCON 2009 and PostgreSQL West 09. Describes SQL solutions to a selection of object-oriented problems:
- Extensibility
- Polymorphism
- Hierarchies
- Using ORM in MVC application architecture
These slides are excerpted from another presentation, "SQL Antipatterns Strike Back."
Enhancements to the QOREX Perspective wheelPhil Trickey
We have recently made some enhancements to the QOREX Perspective wheel which are explained here;
The Perspective Wheel lets you see how everything that your business is doing comes together to deliver your Objectives and Vision.
The outer three layers represent the Objectives, Results, and Enablers that you have added to QOREX. As long as these entities have been linked together correctly (e.g. your Enablers have been linked to Results, and your Results have been linked to Objectives), they will appear in the wheel. In the centre of the wheel, you’ll see two further layers:
The Vision Statement sets the context for your instance of QOREX. It should represent, in one short sentence, what your Objectives, Results, and Enablers are all about.
Perspectives (sometimes called Dimensions) group your Objectives into categories. By default, the wheel uses the four Perspectives of the Business Lifesystem® model: Customer, Proposition, Capability, and Finance.
Chem Draw is a molecule editor first developed in 1985. It allows users to intuitively draw two-dimensional representations of organic molecules. Chem Draw is part of the Chemoffice Suite of programs and is available for Macintosh and Windows. It provides functions for drawing chemical structures, converting between names and structures, and simulating NMR and mass spectra. Chem Draw is widely used by chemists for drawing chemical structures in reports, publications, and theses due to its ease of use and ability to create high quality chemical drawings.
The document discusses Salesforce.com, the world's leading sales application. It notes that Salesforce.com has strong growth in new customers, over 2 million users worldwide, and is a public company traded on the NYSE. It also summarizes that customers report success across key metrics after implementing Salesforce.com solutions.
Detailed information on the operation of the Data Harmony Thesaurus Master module from Access Innovation’s, Inc. Presented by Alice Redmond-Neal and Jack Bruce at the 2012 Data Harmony User Group meeting on February 7, 2012 at the Access Innovations, Inc. offices.
The document discusses various topics related to Microsoft Office applications like Word, Excel, PowerPoint, and networking topologies. It provides information on the different tabs and functions available in Word like the Home, Insert, Design, and Review tabs. It also describes features of Excel, PowerPoint, networking topologies including bus, ring, mesh and star, and basics of other applications like Google Apps and Blogger. Various questions and their answers are provided at the end related to terms used in the document.
Anatomy.TV host Primal Pictures award winning software, this PDF will take new subscribers through the program - from signing in to selecting and using your chosen product within your subscription.
If you are experiencing any problems logging in or using Primal Pictures software please email info@primalpictures.com
This document provides a summary of the Amaya User Manual. It describes the main features of the Amaya editor, including browsing capabilities, viewing and selecting documents, creating and opening documents, and using forms. The manual is organized as a book that can be printed. It focuses only on the specific aspects of Amaya rather than common functions in other programs.
The document provides instructions for creating a multi-page website in Dreamweaver CS6. It discusses setting up the site structure with a root folder and subfolders, creating page templates, and linking pages within the site. The first two pages of the site are designed - an index page and a 'New Arrivals' page. Templates are used to maintain consistency across pages. Links are added between the homepage and the 'New Arrivals' page to allow navigation between them. The footer is also standardized across pages.
In Android, the user interface is built using a hierarchy of View and ViewGroup objects. Views are basic UI elements like buttons and text fields, while ViewGroups serve as containers to hold other views and arrange their layout. The UI hierarchy is defined using XML layout files, which map XML elements to their corresponding View classes. These layout files are loaded and inflated into views at runtime. Views can also be created and added programmatically in code. Views handle drawing, interaction events, and other behaviors to display the UI to the user.
社會網絡分析UCINET Quick Start Guide
This guide provides a quick introduction to UCINET. It assumes that the software has been installedwith the data in the folder C:\Program Files\Analytic Technologies\Ucinet 6\DataFiles and this hasbeen left as the default directory.
Source : https://sites.google.com/site/ucinetsoftware/home
Have you ever asked yourself, “Why doesn’t Revit do that quicker?” Have you ever wished you could pull a lot of external data into Revit at one time? Have you ever wished that you could program Revit without being a coder?
This document provides an introduction to Adobe PageMaker, including definitions, features, advantages, disadvantages, and how to use it. PageMaker is a desktop publishing program that allows users to design documents like books, brochures, and newsletters. It includes tools for placing images, data merging, improved PDF support, and updated filters. The document also explains how to create a new PageMaker document and utilize various palettes that control objects, text, styles, layers, master pages, and hyperlinks.
The document provides an overview of Microsoft PowerPoint, including:
- PowerPoint allows users to create presentations consisting of slides, handouts, speaker notes, and outlines.
- The PowerPoint window includes tabs for inserting content and formatting slides, as well as tools for reviewing and presenting.
- Users can navigate between slides using the slide navigation pane or outline view.
Datastream professional getting started guideIta Kamis
This document provides an overview of how to access and navigate the Thomson Reuters Datastream Professional platform. It describes the main features and functionality available, including:
- Using the Navigator search tool to find financial data series
- Accessing news, research, estimates and economic calendars for selected series
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Ms Word 2010 Training In Ambala ! Batra Computer Centrejatin batra
Batra Computer Centre is An ISO certified 9001:2008 training Centre in Ambala.
We Provide Best Ms Word 2010 Training in Ambala. BATRA COMPUTER CENTRE provides best training in C, C++, S.E.O, Web Designing, Web Development and So many other courses are available.
This document provides an overview of the basic terminology and tools in Microsoft PowerPoint. It discusses how to get started with PowerPoint and open a new presentation. It then summarizes the main menus and tools for formatting text, inserting images, tables, charts and other objects. The tools covered include changing fonts, formatting text, inserting slides, tables, pictures, and shapes. The document aims to accelerate learning PowerPoint through understanding its menus and ribbon interface.
SAP BusinessObjects Explorer is a web-based application that allows users to search through business data, apply filters to focus on specific key performance indicators (KPIs), and visualize the data in interactive charts. It enables saving personalized exploration views, sharing views with others, and exporting data to applications like Web Intelligence for further analysis and reporting.
The document provides an overview of the features and functionality available within the EBSCOhost interface. It describes the basic search screen and search options, including search modes, limiting searches, and previewing article details and images. It also reviews result filtering and organization, saving search strategies and results, and customizing system preferences.
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Similar to Introduction to Snow Owl - A tool for SNOMED CT (20)
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.
Implementing an HL7 version 3 modeling tool from an Ecore modelSnow 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.
The Logical Model Designer - Binding Information Models to TerminologySnow 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.
Using Snow Owl to Maintain Singapore’s SNOMED CT Extension and Drug DictionarySnow 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.
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.
Singapore Drug Dictionary - Developing and integrating a national drug extens...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.
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.
A simple web-based interface for advanced SNOMED CT queriesSnow 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.
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1. INTRODUCTION TO SNOW OWL –
A TOOL FOR SNOMED CT
Sonja Ulrich, Orsolya Bali – B2i Healthcare
11th October 2013
2. Perspective
A perspective is a combination of views and
editors. It defines the layout of the visual
components. The size and position of the visual
components can be easily changed.
The Authoring perspective is the default
setting.
It gives access to a range of functionalities such
as browsing, authoring, creating reference sets,
bookmarking, and working with tasks.
4. Pre-set perspectives
Snow Owl provides several pre-set perspectives
Authoring perspective
Mapping perspective
Reference set perspective
Value set perspective
6. Views
Views are visual components that are typically
used to navigate a list or hierarchy of information
(such as the SNOMED CT Concepts), or display
information for the active editor.
12. Editors
Editors display detailed information about a
resource and are used to make changes to it.
The visual presentation might be a concept
editor (e.g. terminology concepts or reference
sets), a text editor (e.g. scripts), or a task editor.
Editors are launched by double-clicking on a
resource in a view, e.g. a SNOMED CT concept
or a file. They are displayed in the middle part of
the user interface.
14. Hands-on Snow Owl
Change the look of the user interface
Click X next to the title to close a view or editor
Drag the side of a view to resize it
Grab tab of a view to move it to a different spot
Double-click the tab on the top of a view or editor
to maximize it
Reset perspective: Go to Window > Reset
Perspective
15. SNOMED CT concept view
The SNOMED CT view
organizes concepts into a tree
by their IS A relationships. The
default setting shows only the
top-level concepts of the
SNOMED CT terminology.
Each top-level concept has its
own dedicated icon which is
also used for its children. This
allows distinguishing top-level
hierarchies from each other.
16. Browsing the SNOMED CT hierarchy
Expanding a node
reveals the children of a
concept allowing to
browse down the
hierarchy.
Click on a triangle to
expand or collapse the
hierarchy.
Note: If you are using
windows the nodes look
a bit different (plus
signs).
17. Browsing the SNOMED CT hierarchy
Other terminologies and
classification systems can be
browsed the same way.
18. Parents view
The Parents view displays the parents
of a concept.
The selected concept is displayed on
the top and its parent or parents on the
bottom.
Expand the nodes to reveal parent
concepts and browse up the hierarchy.
The Parent view is linked to the
SNOMED CT view, whenever a
concept is selected in the navigator its
parents will be automatically displayed
in the parent view.
This feature also applies to the other
concept navigators (e.g. ATC, ICD-10).
19. Bookmarking
Favorite SNOMED CT concepts, descriptions, ATC, ICD-10, LOINC codes,
and reference sets can be bookmarked for future reference (just like the
bookmarks in your internet browser).
Right-click a concept in the view and select Add bookmark.
20. Bookmarking
A small blue decorator indicates
bookmarked concepts.
Bookmarked concepts can be opened from the Bookmark view. Right-click
and select Delete to remove a bookmark.
21. Hands-on Snow Owl
Get familiar with the SNOMED CT view
Expand nodes in the tree, collapse nodes
Click a concept and see parents in parent view
Set concept as a root
Bookmark a concept
Open a concept from bookmark view
Delete a bookmark
22. How can I search for a
concept?
There are three different search tools in Snow
Owl
Filter search (part of concept views, filters
selected terminology)
Quick search (comprehensive search through
all terminologies and resources)
Advanced search (various resources and
search criteria, ESCG)
24. Filter search – flat list
Type a search term in the
filter text field on the top
of the view.
The preferred term is
displayed. However, all
descriptions are
considered during
filtering.
The filter also allows to
look up a concept by its
ID.
25. SNOMED CT concept view
Filter search – hierarchical view
Filter results can also
be displayed as a a
tree.
The hierarchical
view shows only the
matching concepts
and their top-level
parent concept.
Concepts that are
between the parent
concept and the
matching concept in
the hierarchy are
omitted.
Use the toggle button
to switch between
views.
26. Hands-On Snow Owl
Filter search
Type a search term in filter
Toggle between hierarchical view and flat list
Look up a concept ID: 286860006
27. Quick search
The Quick Search box on the toolbar allows searching
through all terminologies from a single location.
It can also be accessed by pressing CTRL-4 (Windows)
or CMD-4 (OS X).
28. Quick search
Search results appear
progressively with each
character typed further refining
the search. Matching parts are
highlighted in the search results.
They are sorted by based on
how closely they match and user
search profile preferences.
Autocomplete: A suggested
ending for the search term is
displayed in light grey (here:
paravalvular). Press TAB to
accept the suggestion.
29. Quick search
Results are organized in
different sections according
to the underlying terminology
(e.g. SNOMED CT, ICD-10,
ATC, LOINC) or resource
(e.g. reference sets, value
sets).
Previously selected choices
and bookmarks will also
appear with the search
results in their own sections.
The quick search can be
configured to exclude certain
terminology artifacts (e.g.
reference sets, modules).
30. Hands-On Snow Owl
Quick search
Type search term in quick search field
Review results
Select result and open concept in editor
Use shortcut to display more matches
Mistype, abbreviate a search term
Look up a concept ID: 22298006
31. Advanced Search
The Flashlight button in the main tool bar opens
the advanced search dialog.
32. Advanced Search
The advanced search allows searching on
various kinds of resources:
SNOMED CT concepts, SNOMED CT descriptions,
ICD-10, ICD-10-AM, ATC, LOINC, reference sets,
value sets, local code systems, tasks, and files.
Use the tabs to select the resource.
33. Advanced Search
Searches can be restricted to certain criteria:
Concept ID, FSN, Synonym, Preferred Term,
Synonym, top-level hierarchy, status.
36. Search results
The results of the advanced search are displayed in
the search view. Double-click a result to open the
editor.
37. Hands-on Snow Owl
Advanced search
Bring up SNOMED CT concept search
Look for inactive concepts with the description
“Diabetes”
Look for active concepts starting with dia*
Look for concepts with the FSN “Dressing”
Open a concept from the search view
38. SNOMED CT editor
The concept editor serves two functions:
It displays detailed information on a concept.
It allows to make changes to a concept (e.g.
adding another clinical phrase to describe the
concept, retiring a concept or description).
39. Opening the concept editor from
view
Double-click a concept in the SNOMED CT view
to launch the editor.
40. Opening the editor from quick
search
Clicking a match from the
quick search list also opens
the editor.
41. SNOMED CT concept editor
Multiple editors can be
open at the same time.
Use the tabs on the top
of the editor to switch.
Double-click the tab to
maximize the editor.
Click the x symbol to
close the editor or right-click
to select from
many options to close
editors.
42. Displaying editors side by side
The tabs can also be used to display editors side by side:
Drag the tabs to the side of the editor until a small black arrow appears. The arrow will
indicate where the new editor will be docked.
43. SNOMED CT concept editor
pages
The concept editor
displays information
on a series of pages
(e.g. value domain
membership,
mappings).
Click the tabs on the
bottom to select a
page.
44. Editor – Overview page
The overview page shows
information on the concept's
descriptions, relationships, and
metadata.
It is comprised of three
sections:
• Descriptions
• Properties
• SNOMED CT Properties
The sections can be expanded
or collapsed using the small
triangle next to the section
heading.
45. Editor – Overview page
The descriptions
section shows the
clinical phrases that
describe this concept.
46. Editor – Description section
The description type is displayed on the left (e.g. FSN, Synonym).
The description term is displayed on the right (e.g. Sleep disorder care
management).
A rosette icon indicates the preferred term.
The flag on the section heading indicates the currently active
language dialect (here: Singaporean English).
47. Editor – Properties section
The Properties section
displays the concept's
relationships and
datatype properties.
The property type (here:
Is a, Has focus,
Method) is displayed on
the left.
The property value (e.g.
Care regimes
management) is
displayed on the right.
48. Concept model backed editing
To prevent the
creation of erroneous
relationships, the
editor displays only
attribute relationships
of the predefined
range and domain.
Example: Since
Biopsy sample is a
specimen, only
attributes used to
define specimens are
displayed.
49. Editor – Properties section
The properties section automatically creates entry fields for all relationships and datatype
properties specified in the concept model.
A list with valid values is displayed when clicking into the text field. The editor also validates
erroneous entries and provides a link (here: This property violates the concept model) with
further information.
50. Editor – Overview page
The SNOMED CT
Properties section
displays the Concept ID
and other metadata
such as Module,
Effective time, Status,
Definition status, and
Subclass definition.
51. Editor – Overview page
The overview page can
also be used for editing.
Clicking on the blue
triangles will display a list
of actions (e.g. create a
copy of a description,
inactivate a concept).
52. Editor – Descriptions page
The Description page shows information about the descriptions associated with
the selected concept. Descriptions can be added, modified, deleted and
inactivated. Use the toggle button to show inactive descriptions.
53. Editor – Source relationships
page
Source relationships originate from the selected concept and point to a different
concept. The selected concept is the source. Source relationships are also
displayed in the properties section of the overview page. They can be viewed and
edited on this page.
54. Editor – Destination relationships
page
Destination relationships originate from a different concept and point to the given
concept. The selected concept is the target. The page is read-only, if you want to
edit the relationships displayed here, you need to open the source concept.
55. Editor – Value domain membership
page
The Value domain membership page shows if the concept is a member of a
reference set or value set.
56. Editor – Mappings page
The Mappings page shows if the concept is part of a map.
57. Editor – References page
The References page shows if the concept, its descriptions or relationships are
referenced in any reference sets, e.g. language acceptability reference sets.
Note: If the concept is a member of a query or simple type reference set, the
membership will be displayed on the value domain membership page.
58. Hands-on Snow Owl
Get familiar with the editor
Open multiple concepts in the editor
Use tabs on top to switch between editors
Link editor to SNOMED CT view (click link button)
Click tabs on bottom to review different pages
Open: Angina (disorder). Review reference set membership
(value set tab) and mapping
Right-click editor tab to “Close all”
59. Editing concepts
Editing an existing concept
Adding a description
Changing the preferred term
Creating a new concept
60. Adding a synonym
Open concept in editor. Click the Add unsanctioned description icon.
Click into empty text box.
Enter “Synonym” in the text box on the left, and a new description term
in the text box to the right.
Save and enter a commit comment.
61. Adding a synonym
Review the new synonym in the editor
The box around the description term (here: Hantavirus) indicates an
unpublished change. This means that the change has been saved in the
repository but not been formally published yet. Unpublished components
don’t have an effective time assigned. Once a concept has been published,
only the text appears in the editor.
62. Changing the preferred term
Click the blue triangle in front of new preferred term (in this case: Korean haemorrhagic
fever virus)
Chose Set to ... preferred from the actions. Save and enter commit comment.
63. Changing the preferred term
The new preferred term is now displayed in the title of the editor.
It also appears in Quick search and SNOMED CT view.
64. Hands-on Snow Owl
Edit an existing concept
Open a SNOMED CT concept
Add a synonym
Add a definition
Change the preferred term
65. Creating a new concept
Right-click on desired parent concept. Select “Add new child concept” from
context menu.
66. Creating a new concept
Review the new
concept in the editor.
An IS A relationship to
the parent concept
was generated as well
as unique concept ID.
The new concept has
identical SNOMED CT
descriptions. This
way, only differing
information has to be
entered.
67. Creating a new concept
Enter
information for
the new
concept in the
editor. The
FSN has to be
unique.
Save and enter commit
comment.
This action submits the
change to the repository
where it is maintained.
68. Creating a new concept
.. and the SNOMED CT view.
The new components and be reviewed in the history view
69. Hands-on Snow Owl
Create a new SNOMED CT concept
Right-click a concept in the SNOMED CT view to
create a child concept
Enter new FSN, enter new synonym,
Save, enter commit comment
Review new concept in SNOMED CT view
Review new concept in history view
70. Reference sets
Snow Owl supports the creation of reference sets
based on the RF2 specifications. When working with
reference sets you will primarily use
The Reference set perspective which is useful to
manage reference sets in general.
The Reference set view as an overview of the
reference sets in the repository.
The Reference set editor to manage the
members. The user interface of the editor
changes, dependent on the type of reference set.
71. Reference set perspective
Click the shop icon in the
main toolbar to bring up
the reference set
perspective.
It displays the reference
set view to the left and
the editor to the right.
72. Reference set view
Existing reference sets and maps
are displayed in the Reference
Sets view.
Reference sets are sorted by type:
• Simple type
• Attribute value type
• Query type
Click the triangle to expand or
collapse the categories.
Double-click a reference set to
open the editor.
73. Reference set editor
The reference set
editor contains
components from
the SNOMED CT
concepts view
and
the SNOMED CT
concepts editor.
74. Reference set editor – left side
The left side shows the referenced
components as a hierarchy.
The navigation is similar to the SNOMED CT
view:
Click the small triangles to expand or
collapse the tree.
Click a concept to display more information
on the right side.
A filter search can be performed by typing a
term in the text box.
The toggle button allows to switch between
hierarchical view and flat list.
75. Reference set editor – right
side
The right side displays
information about a selected
concept.
Layout and functions are similar
to the overview page of the
SNOMED CT Concept editor:
Click the black triangles to
expand or collapse the
sections.
Click the blue triangles to
open a list with actions for
editing the selected concept.
76. Hands-on Snow Owl
Get familiar with reference sets
Open the reference set perspective
Expand nodes in reference set view
Open a simple type reference set (e.g.
Cardiology)
Review members in the reference set editor
Click member to see it in the reference set editor
Double-click member to open the concept editor
77. Creating a new reference set
Click an icon in the toolbar to create a reference set.
78. Different kinds of reference sets
The simple type reference set is a plain
grouping of concepts by user preferences.
An attribute value type reference set allows
associating a value concept with the referenced
component. It can be used to extend the
ontology with custom properties on the concept.
The members of a query type reference set
are determined based on a semantic query.
79. Creating a simple type reference
set
To create a simple type reference set click the notebook icon on the main
tool.
The wizard automatically creates a reference set identifier concept with the
title as a description.
Type a reference set description (here: Shoulder Reference Set), and select
SNOMED CT Concept as the referenced component type.
Click Finish to proceed.
80. Creating a reference set
The new reference set
will appear in the
reference set view.
The editor is
automatically opened.
The list of reference set
members in the editor is
empty since there were
no referenced
components added yet.
81. Adding members to a reference
set
To add referenced components go to the SNOMED CT View and choose a
concept from the hierarchy (here: Finding of shoulder region).
Right-click the concept and select Add concept and descendants to the active
reference set. This will add the concept and all of its children to the reference
set.
If you want to add only this particular concept use Add concept to the active
reference set.
The context menu is also
available in the Search View.
82. Adding members to a reference
set
A concept can also be dragged from the SNOMED CT Concepts view
and dropped into the Reference set editor. This will add only the selected
concept.
Search results can also be added this way from the search view. Press
CTRL (Windows) or CMD (Mac) to select multiple search results and
drag them into the editor.
83. Deleting and inactivating
members
Right-click a member to remove it from the reference set. You can also remove a
member and its descendants.
Click in the status column to inactivate a member. Published reference set
members can only be inactivated, not deleted.
84. Hands-on Snow Owl
Create a reference set and add members
Create a new simple type reference set
Find new reference set in reference set view
Add members from SNOMED CT view (drag and
drop, context menu)
Delete member
Save and close reference set
85. Creating a query type reference set
The members of a query type reference set are
determined based on a semantic query.
They can be automatically updated when a new
version of SNOMED CT is released.
86. Creating a query type reference
set
To create a query type reference set click the box icon on the main tool.
The wizard automatically creates a reference set identifier concept with the
title as a description. Type a reference set description (here: Query reference
Set).
87. Adding queries
Click the Add member icon the toolbar of the editor to bring up the
wizard.
Click Browse to select a
query.
88. Query type reference set
A query type reference set can include multiple ESCG queries and
therefore contain references to multiple simple type reference sets.
When a new version of SNOMED CT is released, all the reference
sets can be updated by using the Update to current ontology button in
the toolbar.
89. Query type reference set
This action will display any
changes that would be
included by re-running the
queries.
The changes can be
reviewed and selected to
update the corresponding
reference sets.
90. Introduction to semantic queries
Snow Owl includes an editor and execution environment
for Extended SNOMED CT Compositional Grammar
(ESCG) expressions.
ESCG is a formal grammar to compose expressions that
include operators and defined concept identifiers. It can
be used for semantic querying.
All of the operators and grammar constructs are
supported as defined in the NHS LRA terminology
binding specification, which is itself an extension of the
HL7 TermInfo specification.
Concepts can be queried by their relationships, as
opposed to their human readable descriptions.
92. Sample queries
The free Snow Owl download contains a B2i examples folder with
sample queries.
Go to Project Explorer View, and open the B2i Folder. Double-click
All SNOMED CT Concepts.escg to open the editor and see the
query script.
93. Query results
Click Execute button in the toolbar to run the query.
Review query results in the Search view. The results
comprise all SNOMED CT concepts including the root
concept.
94. Creating a new .escg file
Right-click on project folder
Select New > File. Enter a file name
with an .escg extension (e.g.
Findings.escg).
Create a project in the
Project explorer (New >
Project).
95. Entering query script
Double-click the file to launch the empty expression editor.
Type the operator < in the text field.
Drag Clinical Finding from the SNOMED CT view into the
editor. Concept ID and optional text will be automatically
added to the query. This query retrieves all clinical
findings.
<404684003|Clinical finding|
It might be useful to save the query script, so that you can
easily update your search results when release data are
changing.
96. Content assist
Hit Ctrl + Space to
bring up content
assist.
Only operators that
can be used at the
active part of the
query are displayed.
Content assist also
includes a quick
search to find
concepts.
97. Refinement
The refinement operator (:) is usually used in combination
with the attribute value operator (=). These operators are
useful to restrict a query to concepts with certain attributes.
Example: All Clinical findings that have a Finding site
relationship with the target concept being the
Cardiovascular system.
<<404684003|Clinical finding|:
363698007|Finding site| = <<113257007|Structure of
cardiovascular system|
98. Refinement
It’s also possible to refine the query by adding additional
property constraints using a comma as a separator.
This query retrieves bacterial infectious diseases of the
lung caused by streptococcus pneumonia.
<<87628006|Bacterial infectious disease|:
363698007|Finding site| = <<39607008|Lung structure|,
246075003|Causative agent| = <<9861002|Streptococcus pneumoniae|
99. Retrieving reference set
members
The caret operator ^ will list the members of a
reference set. Here is an example for retrieving the
members of the Cardiology reference set.
^152725851000154106|Cardiology reference set|
100. Intersection
The query below retrieves all Clinical findings that are also
members of the Cardiology reference set.
The intersection operator is used to connect the two
expressions.
<404684003|Clinical finding| + ^152725851000154106
|Cardiology reference set|
101. Excluding concepts
The ! operator is used to omit concepts or members of a
reference set from a query. It excludes the concept behind
it.
All Clinical findings that are not a disease:
<<404684003|Clinical finding| + !<<64572001|Disease|
All Clinical findings that are not a member of the non-human
reference set.
<<404684003|Clinical finding| + !^447564002|Non-human
simple reference set|
102. Excluding concepts
You can also use the exclusion to express negation.
This query will return all Clinical findings that do not have
a Bacteria causative agent. These concepts either do not
have any causative agents at all, or they have a different
causative agent.
<<404684003|Clinical finding|:
246075003|Causative agent| = !<<409822003|Bacteria|
103. Additional features of Snow Owl
Collaborative authoring
Workflow
Value Domains, Local Code Systems,
Mapping sets
Ontology generation framework
Pluggable classifiers
Reporting
Groovy scripting
Integrated help
105. FURTHER INFORMATION
Online videos
http://b2i.sg/download/
Snow Owl on Facebook
http://facebook.com/SnowOwlPlatform
Getting started guide
http://b2i.sg/getting-started-guide/