80 skills found · Page 1 of 3
IHTSDO / SnowstormScalable SNOMED CT Terminology Server using Elasticsearch
b2ihealthcare / Snow Owl:owl: Snow Owl Terminology Server - a production-ready, scalable, FHIR Terminology Service compliant server that supports SNOMED CT International and Extensions, LOINC, RxNorm, UMLS, ICD-10/11, custom code systems and many others
wardle / HermesA library and microservice implementing the health and care terminology SNOMED CT with support for cross-maps, inference, fast full-text search, autocompletion, compositional grammar and the expression constraint language.
IHTSDO / Snomed Database LoaderRepresent SNOMED CT in a different types of databases
IHTSDO / Snomed Owl ToolkitThe official SNOMED CT OWL Toolkit. OWL conversion, classification and authoring support.
IHTSDO / Sct Browser FrontendHMTL & Javascript for the front end for the SNOMED CT Browser.
rorydavidson / SNOMED CT DatabaseRepresent SNOMED CT in a relational database
IHTSDO / Sct Snapshot Rest ApiRest API for SNOMED CT Snapshot views, powered by Node.js, Express & MongoDB
IHTSDO / Health Data AnalyticsSnolytical - Health Data Analytics Demonstrator
drivendataorg / Snomed Ct Entity LinkingWinners of the SNOMED CT Entity Linking Challenge
wardle / HadesA FHIR terminology server.
cthoyt / Umls DownloaderDon't worry about UMLS, RxNorm, SNOMED, or SemMedDB licensing - write code that knows how to download it automatically
wardle / Go TerminologyA SNOMED terminology server and command line tool.
IHTSDO / Snowstorm LiteSnowstorm Lite FHIR Terminology Server
hltfbk / E3C CorpusE3C is a freely available multilingual corpus (Italian, English, French, Spanish, and Basque) of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, and training of information extraction systems. It consists of two types of annotations: (i) clinical entities: pathologies, symptoms, procedures, body parts, etc., according to standard clinical taxonomies (i.e. SNOMED-CT, ICD-10); and (ii) temporal information and factuality: events, time expressions, and temporal relations according to the THYME standard. The corpus is organised into three layers, with different purposes. Layer 1: about 25K tokens per language with full manual annotation of clinical entities, temporal information and factuality, for benchmarkingand linguistic analysis. Layer 2: 50-100K tokens per language with semi-automatic annotations of clinical entities, to be used to train baseline systems. Layer 3: about 1M tokens per language of non-annotated medical documents to be exploited by semi-supervised approaches. Researchers can use the benchmark training and test splits of our corpus to develop and test their own models. We trained several deep learning based models and provide baselines using the benchmark. Both the corpus and the built models will be available through the ELG platform.
IHTSDO / Snomed Ui ExamplesSNOMED CT User Interface examples
IHTSDO / Snomed Query ServiceAn implementation of the SNOMED CT Expression Constraint Language.
NachusS / Snomed2VecNew approach to use Snomed-CT Concept using Word Embedding with Word2vec
IHTSDO / Openai DemoThis demonstration showcases potential applications of artificial intelligence (AI) in the implementation of SNOMED CT
AuDigitalHealth / Sctau Sample ScriptsSample relational database load scripts and SQL queries for processing SNOMED CT-AU RF2 release files.
