ACI-Bench

HuggingFace upload of ACI-Bench, which evaluates a model's ability to convert clinical dialogue into structured clinical notes. This repo contains only the core benchmarking data. The full dataset is available on HuggingFace here. If used, please cite the original authors using the citation below.

Dataset Details

	train	valid	test1	test2	test3
aci	35	11	22	22	22
virtassist	20	5	10	10	10
virtscribe	12	4	8	8	8
total	67	20	40	40	40

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

Dataset Sources

• GitHub: https://github.com/wyim/aci-bench
• Paper: https://www.nature.com/articles/s41597-023-02487-3

Direct Use

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", name="virtassist")

    # ---------------------------------------------------------------------
    # 2) Load ONE subset (config) with ONE split
    # ---------------------------------------------------------------------
    virtassist_train = load_dataset("mkieffer/ACI-Bench", name="virtassist", split="train")

    # ---------------------------------------------------------------------
    # 3) Load TWO subsets (configs), all splits for each
    # ---------------------------------------------------------------------
    two_subsets = {
        "virtassist": load_dataset("mkieffer/ACI-Bench", name="virtassist"),
        "aci": load_dataset("mkieffer/ACI-Bench", name="aci"),
    }

    # ---------------------------------------------------------------------
    # 4) Load ALL subsets (virtassist, virtscribe, aci), all splits each
    # ---------------------------------------------------------------------
    all_subsets = {subset: load_dataset("mkieffer/ACI-Bench", 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", name="aci", split=["train", "test1+test2+test3"])
    
    # load each split separately
    aci_all_test_separate = load_dataset("mkieffer/ACI-Bench", name="aci", split=["train", "test1", "test2", "test3"])

Citation

@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}
}