Terminology Browser and FHIR ValueSet Builder

This is a Streamlit-based web application designed to help users search, explore, and build ValueSets from medical terminologies based on the OMOP CDM vocabulary. It supports exporting results in CSV and FHIR-compliant JSON formats, making it useful for projects involving data harmonization, clinical research, and FHIR-based interoperability.

we have used following vocabularies

ID	CDM	Code (cdm v5)	Name
146	CDM 5	OMOP Genomic	OMOP Genomic vocabulary of known variants involved in disease
82	CDM 5	RxNorm Extension	OMOP RxNorm Extension
34	CDM 5	ICD10	International Classification of Diseases, Tenth Revision (WHO)
21	CDM 5	ATC	WHO Anatomic Therapeutic Chemical Classification
8	CDM 5	RxNorm	RxNorm (NLM)
6	CDM 5	LOINC	Logical Observation Identifiers Names and Codes (Regenstrief Institute)
1	CDM 5	SNOMED	Systematic Nomenclature of Medicine - Clinical Terms (IHTSDO)

You can download the vocahulary files from athena

🔍 Features

• Concept Search

  • Search by concept name or concept code.
  • Filter results by vocabulary (e.g., SNOMED, LOINC, RxNorm).
  • Download results as CSV.

• SNOMED Descendant Finder

  • Find all descendants of one or more SNOMED concepts using concept_ancestor relationships.
  • Search ancestors by name or code.
  • Export descendant lists and build FHIR ValueSets.

• FHIR ValueSet Builder

  • Build FHIR-compliant ValueSets directly from search results or SNOMED descendants.
  • Download generated ValueSets as JSON.

🛠️ Technologies Used

• Streamlit for UI
• SQLite for local terminology database (OMOP vocabularies)
• Pandas for data manipulation
• FHIR R4 standard for ValueSet formatting

🗂️ Folder Structure

📦 project_root/
├── db/
│ └── omop_vocab.sqlite
├──vocabulary
├── app.py
├── init_db_omop.py
└── README.md # This file

🧱 Prerequisites

• Python 3.8+
• OMOP Vocabulary tables exported to omop_vocab.sqlite database.
  • Required tables: concept, concept_ancestor

▶️ Getting Started

1. Clone the repository:

   git clone https://github.com/data2health-infoTerminologyBrowser.git
   cd TerminologyBrowser

2. Download the required vocabularies from athena

3. Create the db

python init_db_omop.py

4. Run app.py

streamlit run "app.py"

5. Open the browser at http://localhost:8501