README.md

---
license: apache-2.0
language:
- en
task_categories:
- text-classification
tags:
- medical
- clinical
- pharmacovigilance
- icsr
- adverse-drug-reaction
- biomedical
- binary-classification
pretty_name: ICSR Detection Dataset (binary, balanced, with entities)
size_categories:
- 100K<n<1M
dataset_info:
  features:
  - name: text
    dtype: large_string
  - name: entities
    dtype: large_string
  - name: label
    dtype: large_string
  splits:
  - name: train
    num_bytes: 452911585
    num_examples: 128370
  - name: validation
    num_bytes: 57675247
    num_examples: 16046
  - name: test
    num_bytes: 57515051
    num_examples: 16048
  download_size: 296294658
  dataset_size: 568101883
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# ICSR Detection Dataset (binary, balanced, with entities)

A balanced English dataset for **binary Individual Case Safety Report (ICSR) detection** in
biomedical literature. Each row is a PubMed / Europe PMC title + abstract, labelled for whether
it constitutes an individual case safety report (a suspected drug/poison → adverse reaction in an
identifiable patient), together with the **entities** that drove the decision.

Intended as a first-pass triage / screening corpus for **pharmacovigilance** literature.

## Fields

| column | type | description |
|--------|------|-------------|
| `text` | string | `"Title\n\nAbstract"` (canonicalised; see *Text normalisation*) |
| `entities` | string (JSON) | `{"patient": [...], "suspect_drug": [...], "adverse_reaction": [...]}` — the extracted spans. Empty lists for most DISCARDs (a DISCARD failed a criterion, so there is nothing to extract). |
| `label` | string | `"ICSR"` or `"DISCARD"` |

```python
import json
from datasets import load_dataset

ds = load_dataset("harshad317/ICSR_dataset")
row = ds["train"][0]
print(row["label"], row["text"][:80])
print(json.loads(row["entities"]))   # entities is a JSON string
```

## Splits

Balanced exactly 50/50 in every split (majority class downsampled, then stratified per class,
seed 42). Split ratio 80 / 10 / 10.

| split | rows | ICSR | DISCARD |
|-------|-----:|-----:|--------:|
| train | 128,370 | 64,185 | 64,185 |
| validation | 16,046 | 8,023 | 8,023 |
| test | 16,048 | 8,024 | 8,024 |
| **total** | **160,464** | **80,232** | **80,232** |

## Labeling definition

An article is labelled **ICSR** only if **all four** hold for the case the article reports
firsthand:

1. **Identifiable patient** — an identifiable individual (age / sex / initials / individually
   described case). Case series of individually described patients count. Aggregate cohorts
   ("n=120 patients", randomized trials reporting pooled results) do **not**.
2. **Suspect agent** — a suspected **drug or poison** administered to / taken by that patient and
   presented as possibly involved. Medical **devices are out of scope**; poisons / toxic
   substances (pesticide, venom, injected oils) **are in scope**. A pathogen by itself (an
   infection) is not a suspect agent.
3. **Adverse event** — a harmful/unintended occurrence in that patient. The underlying disease,
   disease progression, and lack of efficacy do **not** count.
4. **Causal association** — the event is **attributed** to the suspect agent (suspected agent →
   reaction link), not mere co-occurrence. A drug used to *treat* the event is not a suspect drug.

Otherwise the article is **DISCARD**.

## How it was built

The positive and negative classes were pooled from LLM labelling pipelines run over PubMed /
Europe PMC literature, deduplicated by normalised title, and balanced 50/50.

Two labelling pipelines contributed labels:
- **Entity-extraction + deterministic rule** (GPT-5.4, high reasoning): extract patient / suspect
  drug / adverse event / causal link, then apply the four-criteria rule above.
- **Decomposed relabelling**: cheap per-entity extraction (GPT-5.4-nano) → strong causal judge
  (GPT-5.4, high reasoning) → deterministic rule, with confidence-based routing. Rows below a
  confidence threshold were **quarantined and excluded**, not guessed.

Negatives include a deliberately large share of **hard negatives** — drug-related biomedical
literature that is *not* an ICSR — to force content discrimination rather than keyword matching.

### Text normalisation

Source pipelines stored text in different formats (`"title: X\n\nastract: Y"`,
`"Title: X\n\nAbtract: Y"`, and raw `"Title\n\nAbstract"`). All rows were normalised to a single
canonical `"Title\n\nAbstract"` form. Without this the prefix format correlated with the label
source, letting a classifier shortcut on formatting instead of content.

## ⚠️ Limitations — read before using

- **The labels are LLM-generated and NOT validated against a human gold standard.** Their accuracy
  is **unmeasured**. Treat them as machine-generated annotations, not ground truth.
- **Two labelling pipelines** contributed labels and disagree on a non-trivial fraction of
  articles. **~4,900 articles were labelled ICSR by one pipeline and DISCARD by the other**; these
  conflicts were resolved **ICSR-wins** (recall-leaning), so some are likely false positives.
- **Same-family dependence.** All labels derive from OpenAI models; shared systematic blind spots
  will not have been caught.
- **Known error modes** (not fully mitigated): the small model's `suspect` vs `treatment` **role**
  call (the largest driver of label changes); **recall misses on case series**; **pathogens**
  mistyped as suspect drugs.
- **Uncertain rows were dropped, not resolved** — the retained set is *easier* than the real-world
  distribution, so models may underperform on genuinely ambiguous cases (the ones that matter most
  in pharmacovigilance).
- **Balance is artificial.** The natural ICSR rate is far below 50%; precision on this balanced
  test split will overstate deployment precision on an unbalanced stream.
- **Deduplication is title-based.** Near-duplicate articles with differing titles may appear across
  splits and could inflate test scores.
- **Truncation.** Very long documents were truncated during labelling.
- **English biomedical titles + abstracts only.**

## Intended use

- First-pass filter / triage for pharmacovigilance literature screening.
- Biomedical NLP research on adverse-event, causality, and entity extraction.

## Out of scope

- **Not a medical device.** Not a regulatory or clinical determination. Do not use as the sole
  basis for safety reporting or clinical decisions; keep a qualified human in the loop.
- Given the unvalidated labels, do not treat this as a benchmark of record without first
  establishing a human-annotated reference set.

## Source data

Derived from openly available PubMed / Europe PMC titles and abstracts (published biomedical
literature; no patient-identifying data beyond what appears in published case reports).