---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
language:
- en
tags:
- machine-unlearning
- medical
- clinical
- privacy
- benchmark
pretty_name: AMNESIA
size_categories:
- 10K<n<100K
configs:
- config_name: random
  data_files:
  - split: forget_5
    path: forget_5_with_category.jsonl
  - split: forget_10
    path: forget_10_with_category.jsonl
  - split: forget_15
    path: forget_15_with_category.jsonl
  - split: forget_20
    path: forget_20_with_category.jsonl
  - split: forget_25
    path: forget_25_with_category.jsonl
  - split: retain_5
    path: retain_5_with_category.jsonl
  - split: retain_10
    path: retain_10_with_category.jsonl
  - split: retain_15
    path: retain_15_with_category.jsonl
  - split: retain_20
    path: retain_20_with_category.jsonl
  - split: retain_25
    path: retain_25_with_category.jsonl
  - split: hold_out
    path: hold_out_with_category.jsonl
  - split: rest
    path: rest_with_category.jsonl
- config_name: cancer
  data_files:
  - split: forget_5
    path: cancer_forget_5_with_category.jsonl
  - split: forget_10
    path: cancer_forget_10_with_category.jsonl
  - split: forget_15
    path: cancer_forget_15_with_category.jsonl
  - split: forget_20
    path: cancer_forget_20_with_category.jsonl
  - split: forget_25
    path: cancer_forget_25_with_category.jsonl
  - split: retain_5
    path: cancer_retain_5_with_category.jsonl
  - split: retain_10
    path: cancer_retain_10_with_category.jsonl
  - split: retain_15
    path: cancer_retain_15_with_category.jsonl
  - split: retain_20
    path: cancer_retain_20_with_category.jsonl
  - split: retain_25
    path: cancer_retain_25_with_category.jsonl
- config_name: cardiovascular
  data_files:
  - split: forget_5
    path: cardiovascular_forget_5_with_category.jsonl
  - split: forget_10
    path: cardiovascular_forget_10_with_category.jsonl
  - split: forget_15
    path: cardiovascular_forget_15_with_category.jsonl
  - split: retain_5
    path: cardiovascular_retain_5_with_category.jsonl
  - split: retain_10
    path: cardiovascular_retain_10_with_category.jsonl
  - split: retain_15
    path: cardiovascular_retain_15_with_category.jsonl
- config_name: infectious_or_immune_diseases
  data_files:
  - split: forget_5
    path: infectious_or_immune_diseases_forget_5_with_category.jsonl
  - split: forget_10
    path: infectious_or_immune_diseases_forget_10_with_category.jsonl
  - split: forget_15
    path: infectious_or_immune_diseases_forget_15_with_category.jsonl
  - split: forget_20
    path: infectious_or_immune_diseases_forget_20_with_category.jsonl
  - split: retain_5
    path: infectious_or_immune_diseases_retain_5_with_category.jsonl
  - split: retain_10
    path: infectious_or_immune_diseases_retain_10_with_category.jsonl
  - split: retain_15
    path: infectious_or_immune_diseases_retain_15_with_category.jsonl
  - split: retain_20
    path: infectious_or_immune_diseases_retain_20_with_category.jsonl
---

# AMNESIA: A Large-Scale Medical Unlearning Benchmark Suite with Disease-Informed Analysis

AMNESIA is the first large-scale, open-source benchmark for **machine unlearning** in the medical domain. It is designed to evaluate methods that selectively remove the influence of specific patient data from trained medical language models while preserving the model's overall clinical knowledge and utility — a capability motivated by privacy regulations such as the GDPR "right to be forgotten."

The benchmark is built on de-identified clinical notes drawn from PubMed Central and paired with model-answerable question–answer items, organized into forget/retain partitions at multiple removal ratios and across disease categories.

- **Paper:** [AMNESIA: A Large-Scale Medical Unlearning Benchmark Suite with Disease-Informed Analysis](https://arxiv.org/abs/2605.30599). Accepted to EMNLP 2026 Main Conference.
- **License:** CC BY-NC-SA 4.0

## Dataset structure

Each line of every `.jsonl` file is a single JSON record built from a de-identified patient note. A record contains:

- a de-identified clinical **patient note** from PubMed Central,
- **factual questions** testing direct recall from the note,
- **reasoning questions** requiring multi-hop clinical inference,
- **gold-standard answers** as verified references, and
- a **disease category** label (one of the CDC-classified disease types).

## Splits

The suite provides two kinds of partitioning, exposed here as loadable configurations:

**Random (patient-level) partitioning.** Patients are selected at random to form forget sets at 5%, 10%, 15%, 20%, and 25%; the remaining patients form the corresponding retain sets. `hold_out` contains held-out QA items that are never seen during training or unlearning, used to test generalization. `rest` holds the remaining data.

**Disease-level partitioning.** The same forget/retain ratios are applied *within* individual disease cohorts — **Cancer**, **Cardiovascular**, and **Infectious/Immune Diseases** — enabling disease-informed analysis. (Note that the Cardiovascular cohort provides the 5/10/15% ratios, while Cancer and Infectious/Immune provide additional higher ratios, reflecting cohort sizes.)

The forget sets are **nested**: each `forget_X` is a subset of the next larger `forget_(X+5)`, which makes evaluations across removal ratios directly comparable and reproducible.

## Usage

```python
from datasets import load_dataset

# Random patient-level splits
ds = load_dataset("dsaeedeh/AMNESIA", "random", split="forget_10")

# Disease-level splits
cancer = load_dataset("dsaeedeh/AMNESIA", "cancer", split="forget_10")
cvd    = load_dataset("dsaeedeh/AMNESIA", "cardiovascular", split="retain_5")
```

You can also stream individual files directly:

```python
ds = load_dataset("json", data_files="hold_out_with_category.jsonl")
```

## Citation

If you use AMNESIA, please cite:

```bibtex
@article{davoudi2026amnesia,
  title   = {AMNESIA: A Large-Scale Medical Unlearning Benchmark Suite with Disease-Informed Analysis},
  author  = {Davoudi, Saeedeh and Iranmanesh, Reihaneh and Frieder, Ophir and Goharian, Nazli},
  journal = {arXiv preprint arXiv:2605.30599},
  year    = {2026}
}

```

## License

Released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Non-commercial use only, with attribution and share-alike.