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rapid-elastic

Disease Synonyms Curation

  • Disease names and synonyms were HUMAN expert curated and assisted via ChatGPT with additional checks within NLM GeneReviews, OrphaNet, and Google searches.
  • disease_names_expanded.json is pre-built and recommended for use without modification.
  • disease_names.py assists curation if changes need to be made: read spreadsheet CSV, write disease names JSON, recommend GPT4 prompts, find duplicates, and merges curated JSON.

Quickstart

  1. configure ENV variables query your Elasticsearch server (within your VPN/firewall)

    1. ELASTIC_HOST : default http://localhost:9200
    2. ELASTIC_USER : basic auth
    3. ELASTIC_PASS : basic auth
  2. pipx install rapid-elastic

  3. rapid-elastic

Elasticsearch server

Read the server setup docs.

KQL (Kibana Query Language)

  • By default, synonyms used to match any "exact phrase" in the note.
  • see kql_syntax.py for alternate methods of building Elasticsearch queries.

Each Elasticsearch hit is saved to "output" folder

  • subject_ref (FHIR Patient.id)
  • encounter_ref (FHIR Encounter.id)
  • document_ref (FHIR DocumentReference.id)
  • group_name (optional)
  • document_title (optional)

SQL Athena

SQL tables are generated from spreadsheet CSV files

  • disease_name
  • orpha_code
  • icd10_code

FHIR resources are referenced using

  • subject_ref (FHIR Patient.id)
  • encounter_ref (FHIR Encounter.id)
  • documentreference_ref (FHIR DocumentReference.id)

FHIR Condition.code is selected using

  • icd10_code

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