Clinical text search elk

Skill rbr7/MedClawMini/skills/clinical-text-search-elk

A focused, production-minded library of 197 clinical-AI and healthcare data-science skills for the OpenClaw agent platform featuring data quality, clinical NLP, big-data ML, explainable AI, drug safety, and regulatory.

Install

npx -y skills add rbr7/MedClawMini --skill clinical-text-search-elk

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Build and query a clinical document search system on the ELK stack (Elasticsearch / Logstash / Kibana) or OpenSearch. Covers index mapping for medical text, custom analyzers with medical synonyms and abbreviation expansion, BM25 relevance, hybrid lexical + semantic (kNN/vector) search, faceted filtering, and Kibana dashboards. Use to make a corpus of notes, reports, or literature searchable, to power retrieval for downstream NLP/RAG, or to stand up a clinical evidence search UI.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.4 KB, as published. Nobody here has run it

Clinical Text Search (ELK / OpenSearch)

Overview

A pile of notes nobody can search has no value. This skill stands up a production-style search index for clinical text using the ELK/OpenSearch stack, tuned for medical language (synonyms, abbreviations, negation-aware filtering) and offering both keyword (BM25) and semantic (vector kNN) retrieval. It doubles as the retrieval layer for RAG and downstream NLP.

When to Use This Skill

Making a corpus of notes, pathology/radiology reports, or literature searchable.
Powering retrieval for a clinical QA / RAG assistant.
Building a faceted evidence-search UI (filter by date, specialty, code).
Operational log/observability search (the ELK stack's home turf) for a data platform.

Design

Index mapping text fields with a custom analyzer; keyword fields for facets (specialty, encounter_type, ICD-10); dense_vector for embeddings.
Medical analyzer lowercasing, a synonym filter (MI ↔ myocardial infarction, HTN ↔ hypertension), abbreviation expansion, and clinical stop-words.
Relevance BM25 with field boosting (impression > body); optional rescore.
Hybrid search combine BM25 with kNN vector similarity (embed with a clinical/ sentence-transformer model) and fuse scores (RRF) for better recall on paraphrases.
Filtering structured facets and date ranges; respect access controls for PHI.
Kibana dashboards for corpus stats, top queries, and result quality monitoring.

Example

from elasticsearch import Elasticsearch
es = Elasticsearch("http://localhost:9200")

es.indices.create(index="clinical_notes", body={
  "settings": {"analysis": {
    "filter": {"med_syn": {"type": "synonym", "synonyms": [
        "mi, myocardial infarction", "htn, hypertension", "t2dm, type 2 diabetes"]}},
    "analyzer": {"clinical": {"tokenizer": "standard",
        "filter": ["lowercase", "med_syn"]}}}},
  "mappings": {"properties": {
    "note_text": {"type": "text", "analyzer": "clinical"},
    "specialty": {"type": "keyword"},
    "service_date": {"type": "date"},
    "embedding": {"type": "dense_vector", "dims": 384, "index": True, "similarity": "cosine"}}}})

# Hybrid query: BM25 + kNN
es.search(index="clinical_notes", body={
  "query": {"bool": {"must": {"match": {"note_text": "chest pain rule out MI"}},
                     "filter": {"term": {"specialty": "cardiology"}}}},
  "knn": {"field": "embedding", "query_vector": qvec, "k": 10, "num_candidates": 100}})

Evaluation

Measure retrieval with precision@k, recall@k, MRR, and nDCG against a labeled query set. A/B the synonym pack and the BM25-vs-hybrid configuration (use ab-testing-healthcare for the rollout). Monitor zero-result and long-tail queries in Kibana to grow the synonym list.

Outputs

A configured clinical_notes index + reusable mapping/analyzer JSON.
synonyms.txt curated medical synonym/abbreviation pack.
retrieval_eval.md P@k / nDCG by configuration.
Kibana dashboard export for corpus and query monitoring.

Healthcare Context

Tuned for clinical vocabulary and the operational reality of PHI access control and audit logging. Retrieval feeds clinical-text-summarization and RAG assistants; the same stack covers data-platform log search. Use OpenSearch as the Apache-2.0 drop-in where ELK licensing matters.

References

Elasticsearch / OpenSearch docs https://www.elastic.co/guide / https://opensearch.org/docs
Robertson & Zaragoza (2009), BM25 and Beyond.
Reciprocal Rank Fusion (Cormack et al. 2009) for hybrid search.