Searches and fetches PubMed and PMC via NCBI E-utilities (ESearch then EFetch/ESummary) to gather biomedical evidence and build text corpora. Use when the user wants citations for a condition or drug, abstracts to summarize, MeSH-based searches, or a corpus of literature to run NER over. Trigger keywords: PubMed, PMC, NCBI, E-utilities, ESearch, EFetch, ESummary, MeSH, PMID, literature search, abstracts, evidence. Pairs adjacent to OpenMed: fetched abstracts feed openmed.analyze_text for biom...
Scanned 9/12/2026
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---
name: mining-pubmed-literature
description: "Searches and fetches PubMed and PMC via NCBI E-utilities (ESearch then EFetch/ESummary) to gather biomedical evidence and build text corpora. Use when the user wants citations for a condition or drug, abstracts to summarize, MeSH-based searches, or a corpus of literature to run NER over. Trigger keywords: PubMed, PMC, NCBI, E-utilities, ESearch, EFetch, ESummary, MeSH, PMID, literature search, abstracts, evidence. Pairs adjacent to OpenMed: fetched abstracts feed openmed.analyze_text for biomedical NER, and OpenMed-extracted diagnoses/drugs/genes become the search terms. E-utilities are public; an optional free API key raises rate limits from 3 to 10 requests/second."
license: Apache-2.0
metadata:
project: OpenMed
category: research-genomics
pairs: adjacent
version: "1.0"
---
# Mining PubMed & PMC literature (NCBI E-utilities)
Search **PubMed** (citations/abstracts) and **PMC** (full text) programmatically
with **NCBI E-utilities** — the stable HTTP interface to Entrez. The core pattern
is two steps: **ESearch** returns matching record IDs (PMIDs), then **EFetch** (or
**ESummary**) downloads the records. The **Entrez History server** (`usehistory=y`)
lets you chain the two without re-sending thousands of IDs.
E-utilities are public. **No key is required**, but a free API key raises your
limit from **3 to 10 requests/second** and is strongly recommended for batch work.
## When to use
- OpenMed extracted a diagnosis, drug, or gene and you want supporting literature.
- You need abstracts to summarize or to assemble a corpus for biomedical NER.
- You want MeSH-anchored, reproducible searches (date ranges, article types).
For ClinicalTrials.gov use `searching-clinicaltrials`; this skill is for the
published literature.
## Quick start (real E-utilities calls)
Base URL: `https://eutils.ncbi.nlm.nih.gov/entrez/eutils/`. JSON for ESearch/
ESummary via `retmode=json`; EFetch returns text or XML (no JSON for PubMed).
```python
import requests, time
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
API_KEY = None # set to your free NCBI key to get 10 req/s instead of 3
def _params(**kw):
if API_KEY:
kw["api_key"] = API_KEY
return kw
def esearch(term: str, retmax: int = 50) -> dict:
"""Find PMIDs; usehistory=y stores them on the Entrez History server."""
r = requests.get(f"{BASE}/esearch.fcgi", params=_params(
db="pubmed", term=term, retmax=retmax,
usehistory="y", retmode="json"), timeout=30)
r.raise_for_status()
res = r.json()["esearchresult"]
return {"count": int(res["count"]), "ids": res["idlist"],
"webenv": res["webenv"], "query_key": res["querykey"]}
def efetch_abstracts(webenv: str, query_key: str, retmax: int = 50) -> str:
"""Pull abstracts by reference to the stored result set (no ID list needed)."""
r = requests.get(f"{BASE}/efetch.fcgi", params=_params(
db="pubmed", WebEnv=webenv, query_key=query_key,
retmax=retmax, rettype="abstract", retmode="text"), timeout=60)
r.raise_for_status()
return r.text
hits = esearch('("type 2 diabetes"[MeSH]) AND metformin AND 2023:2025[pdat]')
print(hits["count"], "papers")
abstracts = efetch_abstracts(hits["webenv"], hits["query_key"])
```
Equivalent cURL (search then fetch one PMID's abstract):
```bash
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=metformin&retmode=json"
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=38000000&rettype=abstract&retmode=text"
```
## ESummary for structured metadata
When you need titles/authors/journal/date as JSON (not the full abstract), use
ESummary — it returns one record per ID:
```python
def esummary(ids: list[str]) -> dict:
r = requests.get(f"{BASE}/esummary.fcgi", params=_params(
db="pubmed", id=",".join(ids), retmode="json"), timeout=30)
r.raise_for_status()
return r.json()["result"] # keyed by PMID: title, pubdate, source, authors…
```
For PMC full text, repeat with `db=pmc` and EFetch `rettype=""`/`retmode=xml`
(JATS XML). Respect each article's license before redistributing full text.
## Workflow
1. **Build the query.** Combine OpenMed-extracted terms with MeSH tags and field
filters: `"<disease>"[MeSH] AND <drug>[tiab] AND 2020:2025[pdat]`. Use
`[tiab]` (title/abstract), `[au]` (author), `[pdat]` (publication date).
2. **ESearch with `usehistory=y`** to capture `WebEnv` + `query_key` and the count.
3. **Batch-fetch** with EFetch/ESummary in pages of ≤ ~200 IDs (or by history),
sleeping to stay under your rate limit.
4. **Parse** abstracts/metadata; store PMID, title, journal, date, abstract text.
5. **NER the abstracts** with `openmed.analyze_text` to extract diseases, drugs,
genes, and oncology entities for downstream synthesis.
## Hand-off to / from OpenMed
- **OpenMed facts → query.** `openmed.analyze_text(note)` yields Disease,
Pharmaceutical, Genomics, and Oncology entities. Turn the top spans into the
ESearch `term` (optionally grounded: ICD-10 label, RxNorm ingredient, gene
symbol) to retrieve targeted evidence.
- **Abstracts → OpenMed.** Feed fetched abstracts straight into
`openmed.analyze_text(abstract, model_name="disease_detection_superclinical")`
(or a Genomics/Oncology model) to structure the literature into entities for
evidence tables or knowledge-graph edges.
- Queries and abstracts are **public** literature, not PHI. Still run locally and
never embed patient text in a search term.
## Edge cases & gotchas
- **Rate limits.** 3 req/s without a key, 10 with one — exceed it and NCBI returns
HTTP 429. Add `api_key`, throttle, and retry with backoff. NCBI also requests a
`tool=` and `email=` parameter identifying your application.
- **EFetch has no JSON for PubMed.** Use `retmode=text` (human-readable) or
`retmode=xml` (PubMedArticle XML) and parse XML for structured fields.
- **History expires.** `WebEnv`/`query_key` are session-scoped — fetch promptly
after searching, or re-run ESearch.
- **Large result sets.** Page with `retstart`/`retmax` (or history) rather than
pulling everything at once; cap total fetches.
- **MeSH lag.** Very recent articles may not yet be MeSH-indexed — include `[tiab]`
term variants so you do not miss them.
- **Full-text licensing.** PMC full text carries per-article licenses; many are
not redistributable. Store PMIDs/abstracts freely; check the license before
republishing full text.
## Standards & references
- E-utilities In-Depth (parameters & syntax) — https://www.ncbi.nlm.nih.gov/books/NBK25499/
- E-utilities Quick Start — https://www.ncbi.nlm.nih.gov/books/NBK25497/
- General introduction & policies — https://www.ncbi.nlm.nih.gov/books/NBK25501/
- API keys & rate limits — https://support.nlm.nih.gov/kbArticle/?pn=KA-05317
- PubMed search field tags — https://pubmed.ncbi.nlm.nih.gov/help/
- MeSH browser — https://meshb.nlm.nih.gov/
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