This document discusses advanced terminology services for FHIR including SNOMED CT. It provides an overview of FHIR terminology including concept maps, code systems, and value sets. It demonstrates Shrimp for browsing terminologies, Snapper for authoring FHIR resources, and discusses implicit value sets, versioning, the Expression Constraint Language, and code system operations like $subsumes and $closure. Practical tips are provided for efficient terminology server usage and handling complex value sets.
SNOMED Bound to (Information) Model | Putting terminology to workKoray Atalag
Prezo I gave at the HL7 New Zealand FHIR and Ice Seminar (latter referring to SNOMED!). I was asked to talk briefly about how information models relate to terminology and also highlight some other information modelling formalisms and initiatives (e.g. openEHR, ISO/CEN 13606, CIMI and DICOM SR).
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.
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.
SNOMED Bound to (Information) Model | Putting terminology to workKoray Atalag
Prezo I gave at the HL7 New Zealand FHIR and Ice Seminar (latter referring to SNOMED!). I was asked to talk briefly about how information models relate to terminology and also highlight some other information modelling formalisms and initiatives (e.g. openEHR, ISO/CEN 13606, CIMI and DICOM SR).
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.
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.
An Ontology-based Decision Support Framework for Personalized Quality of Life...Marina Riga
Publication:
Riga, M., Kontopoulos, E., Karatzas, K., Vrochidis, S., Kompatsiaris, I. (2018) An Ontology-based Decision Support Framework for Personalized Quality of Life Recommendations. In Dargam F. et al. (Eds.) Proceedings of the 4th International Conference on Decision Support System Technology (ICDSST 2018), LNBIP 313, pp. 38–51, Heraklion, Greece, 22–25 May 2018, doi:10.1007/978-3-319-90315-6_4
Production Readiness Strategies in an Automated WorldSean Chittenden
Production Ready. What does it mean? And to whom? Does that term factor in post-launch concerns such as debugability and ownership? What are the lifecycle phases for moving an idea into a hardened production system?
As the world continues its furious adoption of automation, Foo-as-a-Service, and ever changing tools, what are the baseline assumptions, risks, checklists, and processes required to support the evolving landscape of "production ready." In this talk we will deploy a sample application and build both a checklist and scorecard to evaluate the readiness of a system and an organization's practices.
BioCatalogue talk by Carole Goble. She outlines in these slides the reasons behind the BioCatalogue project. And present the BioCatalogue and its goals.
The semantic Web is built on the Resource Description Framework (RDF). RDF is a graph model. It would be expected that a wide range of network analytical tools could be directly applied to a RDF data set. However, most network algorithms assume that a graph does not have parallel edges which the RDF graph model allows. Two approaches will be examined: direct measures of RDF graph structure using ratios and extraction of graphs from an RDF data set. Py-Triple-Simple (http://code.google.com/p/py-triple-simple/), an experimental pure Python library, can extract “well behaved” graphs from an N-triples file and can quantify RDF graph structure using ratios.
This material has been used demonstrate practices with some misunderstanding of RESTful APIs. Let's see some samples and share. Json-patch is also shown with some samples, as it is quite useful for RESTful services.
Keynote lecture on ICD Revision; delivered 7 October 2014; Health Information Management, Coding, Digital ICD; ICD10, ICD11, Ontology, Content Model, Audio notes included.
Getting the best of Linked Data and Property Graphs: rdf2neo and the KnetMine...Rothamsted Research, UK
Graph-based modelling is becoming more popular, in the sciences and elsewhere, as a flexible and powerful way to exploit data to power world-changing digital applications. Com- pared to the initial vision of the Semantic Web, knowledge graphs and graph databases are be- coming a practical and computationally less formal way to manage graph data. On the other hand, linked data based on Semantic Web standards are a complementary, rather than alternative, ap- proach to deal with these data, since they still provide a common way to represent and exchange information. In this paper we introduce rdf2neo, a tool to populate Neo4j databases starting from RDF data sets, based on a configurable mapping between the two. By employing agrigenomics- related real use cases, we show how such mapping can allow for a hybrid approach to the man- agement of networked knowledge, based on taking advantage of the best of both RDF and prop- erty graphs.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
1. Advanced FHIR Terminology Services,
incl. SNOMED CT
AUSTRALIAN E-HEALTH RESEARCH CENTRE, HEALTH & BIOSECURITY
Michael Lawley, PhD | Research Group Leader
16 November 2017
FHIR® is the registered trademark of HL7 and is used with the permission of HL7. The Flame Design mark is the registered trademark of HL7 and is used with the permission of HL7.
2. Who am I?
• Australian eHealth Research Centre, CSIRO
• SNOMED involvement since late 2000s
• Technical Committee 2010-2015
• Modelling Advisory Group 2015-present
• SNOMED Languages Project Group
• SNOMED Logic Profile Working Group
• SNOMED CT URI specification
• Expression Constraint Language
• Ontoserver, Shrimp, Snapper, Snorocket
• Australia’s National Clinical Terminology Service
6. Getting to know each other
• Jim’s Practical Terminology Services this morning?
• Used Terminology services?
• Implemented any part of the Terminology subsystem?
• SNOMED CT knowledge?
• Other code system knowledge?
7. FHIR Terminology - big picture
Concept Map
Defines a set of
concepts with a
coherent meaning
src rel tgt
Code System
Defines a set of
concepts with a
coherent meaning
url, version, name, …
Filters, Properties
Concepts
• Code
• Display
• Definition
Code System
Defines a set of
concepts with a
coherent meaning
url, version, name, …
Filters, Properties
Concepts
• Code
• Display
• Definition
Value Set
A selection of a
set of codes for
use in a
particular
context
Value Set
A selection of a
set of codes for
use in a
particular
context
8. Terminology in FHIR Search
• A lot of search parameters in FHIR are of type ‘token’
• These can leverage terminology services
• :above/:below
• E.g.: /Condition?body-site:below=http://snomed.info/sct|272673000 (all
Conditions where the finding site is a descendent of Bone Structure
• :in/:not-in
• E.g.:
/ProcedureRequest?code:in=http://snomed.info/sct?fhir_vs=refset/3257036
1000036108 (all ProcedureRequests in the Imagine Procedure refset
Basic Terminology Services | Jim Steel8 |
12. Practical tips
1. Beware out-of-order responses
2. Minimise latency
– Batch multiple operations for single round-trip
– Also avoids need for client-side synchronisation
3. Minimise bandwidth
– count for $expand
– includeDefinition, includeDesignations on $expand
– property for $lookup
– _count, _elements for search / read operations
– Accept-Encoding: gzip
4. GET can be cached (HTTP level), POST cannot
Warning: A lot of these can be server-dependent. They’re not proprietary, but you should
check support for them by your terminology server.
13. Implicit ValueSets
Defined for specific code systems
SNOMED CT, LOINC, …
http://snomed.info/sct?fhir_vs
http://snomed.info/sct?fhir_vs=isa/{sctid}
http://snomed.info/sct?fhir_vs=refset
http://snomed.info/sct?fhir_vs=refset/{sctid}
16. SNOMED CT Versioning
• International release (6 monthly)
• National releases (1-6 monthly)
• Other releases
Version is an Edition + Effective Time
http://snomed.info/sct/{module}/version/{date}
18. Implicit ValueSets
Defined for specific code systems
SNOMED CT, LOINC, …
http://snomed.info/sct?fhir_vs
http://snomed.info/sct?fhir_vs=isa/{sctid}
http://snomed.info/sct?fhir_vs=refset
http://snomed.info/sct?fhir_vs=refset/{sctid}
19. Implicit ValueSets
Defined for specific code systems
SNOMED CT, LOINC, …
http://snomed.info/sct?fhir_vs
http://snomed.info/sct?fhir_vs=isa/{sctid}
http://snomed.info/sct?fhir_vs=refset
http://snomed.info/sct?fhir_vs=refset/{sctid}
http://snomed.info/sct/{module}
20. Implicit ValueSets
Defined for specific code systems
SNOMED CT, LOINC, …
http://snomed.info/sct?fhir_vs
http://snomed.info/sct?fhir_vs=isa/{sctid}
http://snomed.info/sct?fhir_vs=refset
http://snomed.info/sct?fhir_vs=refset/{sctid}
http://snomed.info/sct/{module}/version/{date}
25. “Complex” ValueSets
Cycles with ValueSet inclusion / exclusion
Mixing codes from multiple code systems (don’t)
Multiple versions of the same code system
27. Expression Constraint Language (ECL)
http://snomed.org/ecl
Based on post coordination syntax
Wildcard, descendants, ancestors, reference set members, cardinality
constraints
http://ontoserver.csiro.au/shrimp/ecl_help.html
filter:[{property:’constraint’, op:’=‘, value: ‘…’ }]
28. ECL brief summary
Category Syntax Example
Basic sctId, *, ^ sctId ^ 723264001 |lateralizable
body structure|
Hierarchy << sctId, < sctId, <! sctId,
>> sctId, > sctId, >! sctId
<< 404684003 |clinical finding|
Composition x AND y, x OR y, x MINUS y
Relationships a : b = c, a : { b = c, d = e},
a.b
<< 404684003 :
363698007 |finding site| =
^ 723264001
Cardinality a : [low..high] b = c << 404684003 :
[1..1] 363698007 = *
30. CodeSystem operations
$subsumes?codeA= &codeB=
• equivalent, subsumes, subsumed-by, and not-subsumed
• terminology server handles post coordination semantics
$compose
• “Given a set of properties, return one or more possible matching
codes”
Presentation title | Presenter name31 |
32. $closure – usage flow
• init
• add set of codes
• ConceptMap of hierarchy &
equivalence [transitively closed]
• add more codes
• ConceptMap of additional
hierarchy & equivalence
• …
• resynchronisation
• {}
• 0: {}
• {a, c}
• 1: {a subsumes c}
• {b, d}
• 2: {a subsumes b, b subsumes c,
a subsumes d}
33. code:below
• Use $closure to maintain local edge-table of parent-child
relationships
• code:below=123
SELECT C.*
FROM Condition C, Edges E
WHERE C.code = 123
OR (E.parent = 123 AND C.code = E.child);
34. Code Mnemonic Code Mnemonic
equivalent wider
equal subsumes
inexact narrower
unmatched specializes
disjoint relatedto
ConceptMap - equivalence relationships
TS
TS
S T
S T
TS
S
TS
TS
S=T
TS
This tutorial presents advanced use of clinical terminology services in FHIR including SNOMED CT-specific features.
It covers implicit and explicit ValueSets and ConceptMaps, complex ValueSet definitions, and the use of SNOMED CT's Expression Constraint Language (ECL) in ValueSet definitions.
We will also cover the use of $closure to support subsumption-based querying of patient data.
Free service, hosted by CSIRO
Separately licensable
Free service, hosted by CSIRO
Issues with CORS
All using FHIR APIs
$expand + filter
$expand
$lookup
$translate
All codes (including inactives)
Codes by subsumption (so no inactives)
All reference sets
Members of a reference set
Descendants of diabetes
Not problems with SCT in FHIR, but problems with SCT that are highlighted and brought to the fore by the success of FHIR
All codes (including inactives)
Codes by subsumption (so no inactives)
All reference sets
Members of a reference set
Can specify CodeSystem version in ValueSet definition (normally don’t want to do this)
lockedDate doesn’t work for code systems like SNOMED CT
Also not easy for code systems with version formats that are not date-based
Can specify alternate display text
Observed support is patchy
Multiple filters in an include – ALL must be true
Multiple ValueSets – must be in all of them
Excludes -
Excludes -
primitive & fully defined
sufficient conditions
SCG – SNOMED CT Compositional Grammar
Focus concept
Attribute name
Attribute value
“grouping” / “role grouping”
subsumption