32ADECorpus

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).