32_ADE_Corpus
Overview
ADE Corpus V2 — Adverse Drug Event relation extraction dataset from annotated PubMed case reports.
| Field |
Value |
| Category |
Drug-centric |
| Subcategory |
Drug NLP / Text Mining |
| Source |
GitHub |
| Paper |
ACL 2016 |
| Local Path |
resources_metadata/drug_nlp/ADECorpus/ADE-Corpus-V2 |
Data Files
| File |
Content |
DRUG-AE.rel |
Drug ↔ Adverse Event relation pairs with source sentences |
DRUG-DOSE.rel |
Drug ↔ Dose relation pairs with source sentences |
ADE-NEG.txt |
Negative examples (sentences without adverse events) |
Quick Start
from 32_ADE_Corpus import ADECorpus # or rename to ade_corpus
corpus = ADECorpus()
# Single entity
print(corpus.query("aspirin"))
# Multiple entities
print(corpus.query(["lithium", "hepatotoxicity"]))
# Corpus statistics
print(corpus.stats())
Query Input / Output
Input
corpus.query(entities) — accepts str or list[str].
Each entity is matched case-insensitively against both drug names and adverse event names.
Output (JSON)
{
"aspirin": {
"entity": "aspirin",
"matched_as_drug": true,
"matched_as_adverse_event": false,
"total_mentions": 42,
"adverse_events": ["bleeding", "tinnitus", "..."],
"doses": ["100mg", "..."],
"related_drugs": null,
"pubmed_ids": ["12345678", "..."],
"sample_sentences": ["A 65-year-old patient developed ..."]
}
}
| Field |
Description |
matched_as_drug |
Entity found as a drug name |
matched_as_adverse_event |
Entity found as an adverse event name |
total_mentions |
Total matching records |
adverse_events |
List of associated adverse events (when matched as drug) |
doses |
List of associated doses (when matched as drug) |
related_drugs |
List of drugs causing this event (when matched as AE) |
pubmed_ids |
Up to 10 source PubMed IDs |
sample_sentences |
Up to 3 example sentences |
Notes
- All matching is case-insensitive.
query() returns a JSON string directly consumable by LLMs.stats() returns corpus-level counts (total relations, unique drugs/AEs).- No external dependencies — stdlib only (
os, json, collections).
gabrielmoreira