Tips For Asking Natural Language Questions
How to Effectively Use Vyasa’s QA Features
Vyasa-Supported Ontologies
A list of all ontologies currently available off-the-shelf in Vyasa products.
Table of Available Ontologies
| Ontology | Acronym | Author | Version | Last Updated |
| Current Procedural Terminology | CPT | American Medical Association (AMA) | 2020AB | 01/06/2021 |
| Experimental Factor Ontology | EFO | EMBL’s European Bioinformatics Institute (EMBL-EBI) | 3.39.1 | 02/24/2022 |
| Foundational Model of Anatomy | FMA | NIH NLM’s Unified Medical Language System (UMLS) | 5.0.0 | 05/13/2019 |
| Gene Ontology | GO | GO Consortium | 2022-01-13 | 01/18/2022 |
| Human Phenotype Ontology | HPO | Monarch Initiative | 2022-02-14 | 02/14/2022 |
| International Classification of Diseases, Version 9 – Clinical Modification | ICD-9 CM | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| International Classification of Diseases, Version 10 | ICD-10 | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| International Classification of Diseases, Version 10 – Clinical Modification | ICD-10 CM | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| International Classification of Diseases, Version 10 – Procedure Coding System | ICD-10 PCS | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| Logical Observation Identifier Names and Codes | LOINC | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| Medical Dictionary for Regulatory Activities Terminology | MedDRA | MedDRA MSSO | 2021AB | 11/18/2021 |
| Medical Subject Headings | MeSH | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| National Cancer Institute Thesaurus | NCIT | NIH’s National Cancer Institute (NCI) | 22.02d | 03/01/2022 |
| Ontology of Consumer Health Vocabulary | OCHV | Amith et. al (2019) | Version 1.0 | 09/25/2019 |
| Online Mendelian Inheritance in Man | OMIM | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| Orphanet Rare Disease Ontology | ORDO | French National Institute for Health and Medical Research (INSERM) | Version 4.0 | 12/15/2021 |
| PLOS Thesaurus | PLOSTHES | PLOS Taxonomy Team | 2017-1 (BioPortal) 2020-1 (GitHub) | 09/21/2017 (BioPortal) 07/27/2020 (GitHub) |
| RxNORM | RxNORM | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| Systematized Nomenclature of Medicine, International Version | SNMI (SNOMED) | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| Systematized Nomenclature of Medicine, Clinical Terms | SNOMED CT | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
| World Health Organization (WHO) Adverse Reaction Terminology | WHO-ART | NIH NLM’s Unified Medical Language System (UMLS) | 2021AB | 11/18/2021 |
Interested in adding one or several of these ontologies to your instances? Reach out to [email protected] or someone on our support team for assistance.
What are NER Concepts?
Understanding how NER concepts facilitate your analyses.
NER, or Named Entity Recognition, is a deep learning text analytics subdomain where terms and phrases are identified within unstructured text and classified into categorical (aka entity) types (e.g. proteins, cell line, disease, companies, etc.). We use NER tagging to create NER Concepts, which appear in several areas of Layar. Some examples of how we leverage NER Concepts include Filters By Concept Type (Axon Specific), the Document View, and the NER Concept Graph.
Using the Search Bar in Layar
Currently, Layar utilizes keywords and Boolean search parameters to capture documents. Files can be retrieved if the keywords exist in the title, metadata, or (most impressively) within a section of the document.
