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configs:
- config_name: virtassist
default: true
data_files:
- split: train
path: virtassist/train.parquet
- split: valid
path: virtassist/valid.parquet
- split: test1
path: virtassist/test1.parquet
- split: test2
path: virtassist/test2.parquet
- split: test3
path: virtassist/test3.parquet
- config_name: aci
data_files:
- split: train
path: aci/train.parquet
- split: valid
path: aci/valid.parquet
- split: test1
path: aci/test1.parquet
- split: test2
path: aci/test2.parquet
- split: test3
path: aci/test3.parquet
- config_name: virtscribe
data_files:
- split: train
path: virtscribe/train.parquet
- split: valid
path: virtscribe/valid.parquet
- split: test1
path: virtscribe/test1.parquet
- split: test2
path: virtscribe/test2.parquet
- split: test3
path: virtscribe/test3.parquet
---
# ACI-Bench
HuggingFace upload of ACI-Bench, which evaluates a model's ability to convert clinical dialogue into structured clinical notes. This dataset includes the [benchmark itself](https://huggingface.co/datasets/mkieffer/ACI-Bench), as well as data from ablation studies testing different transcription methods. If used, please cite the original authors using the citation below.
## Dataset Details
| subset | transcript_version | train | valid | test1 | test2 | test3 | Total |
| ---------- | ------------------ | ----- | ----- | ----- | ----- | ----- | ----- |
| aci | asr | 35 | 11 | 22 | 22 | 22 | 112 |
| aci | asrcorr | 35 | 11 | 22 | 22 | 22 | 112 |
| aci | humantrans | 0 | 0 | 0 | 0 | 0 | 0 |
| virtassist | asr | 0 | 0 | 0 | 0 | 0 | 0 |
| virtassist | asrcorr | 0 | 0 | 0 | 0 | 0 | 0 |
| virtassist | humantrans | 20 | 5 | 10 | 10 | 10 | 55 |
| virtscribe | asr | 12 | 4 | 8 | 8 | 8 | 40 |
| virtscribe | asrcorr | 0 | 0 | 0 | 0 | 0 | 0 |
| virtscribe | humantrans | 12 | 4 | 8 | 8 | 8 | 40 |
| ALL | ALL | 114 | 35 | 70 | 70 | 70 | 359 |
### Dataset Description
The dataset consists of different subsets capturing different clinical workflows
1) ambient clinical intelligence (`aci`): doctor-patient dialogue
2) virtual assistant (`virtassist`): doctor-patient dialogue with queues to trigger Dragon Copilot, e.g., "hey, dragon. show me the chest x-ray"
3) virtual scribe (`virtscribe`): doctor-patient dialogue with a short dictation from the doctor about the patient at the very beginning
There are three different transcription versions:
1) `asr`: machine-transcribed
2) `asrcorr`: human corrections to `asr`, for example: "nonsmile" in D2N081 --> "non-small" in ACI006
3) `humantrans`: transcribed by a human
The subsets have the following transcription versions
1) `aci`: `asr` and `asrcorr`
2) `virtassist`: `humantrans` only
3) `virtscribe`: `asr` and `humantrans`
### Dataset Sources
- **GitHub:** https://github.com/wyim/aci-bench
- **Paper:** https://www.nature.com/articles/s41597-023-02487-3
### Direct Use
```python
from datasets import load_dataset
SUBSETS = ["virtassist", "virtscribe", "aci"]
SPLITS = ["train", "valid", "test1", "test2", "test3"]
if __name__ == "__main__":
# ---------------------------------------------------------------------
# 1) Load ONE subset (config) with ALL splits
# ---------------------------------------------------------------------
virtassist_all = load_dataset("mkieffer/ACI-Bench-MedARC", name="virtassist")
# ---------------------------------------------------------------------
# 2) Load ONE subset (config) with ONE split
# ---------------------------------------------------------------------
virtassist_train = load_dataset("mkieffer/ACI-Bench-MedARC", name="virtassist", split="train")
# ---------------------------------------------------------------------
# 3) Load TWO subsets (configs), all splits for each
# ---------------------------------------------------------------------
two_subsets = {
"virtassist": load_dataset("mkieffer/ACI-Bench-MedARC", name="virtassist"),
"aci": load_dataset("mkieffer/ACI-Bench-MedARC", name="aci"),
}
# ---------------------------------------------------------------------
# 4) Load ALL subsets (virtassist, virtscribe, aci), all splits each
# ---------------------------------------------------------------------
all_subsets = {subset: load_dataset("mkieffer/ACI-Bench-MedARC", name=subset) for subset in SUBSETS}
aci_all = all_subsets["aci"] # DatasetDict
aci_train = aci_all["train"] # Dataset
aci_valid = aci_all["valid"]
# ---------------------------------------------------------------------
# 5) Load multiple splits at once
# ---------------------------------------------------------------------
# load each split, concatenated
aci_all_test_concat = load_dataset("mkieffer/ACI-Bench-MedARC", name="aci", split=["train", "test1+test2+test3"])
# load each split separately
aci_all_test_separate = load_dataset("mkieffer/ACI-Bench-MedARC", name="aci", split=["train", "test1", "test2", "test3"])
```
## Citation
```bibtex
@article{aci-bench,
author = {Wen{-}wai Yim and
Yujuan Fu and
Asma {Ben Abacha} and
Neal Snider and Thomas Lin and Meliha Yetisgen},
title = {ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation},
journal = {Nature Scientific Data},
year = {2023}
}
``` |