This repository contains the data and source code for:
"ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation". Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Neal Snider, Thomas Lin, Meliha Yetisgen. Nature Scientific Data, 2023.
Paper: https://www.nature.com/articles/s41597-023-02487-3
Dataset: https://figshare.com/articles/dataset/aci-bench-corpus_zip/22494601?file=41498793
@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}
}
The ACI-BENCH collection consists of full doctor-patient conversations and associated clinical notes and includes the data splits from the MEDIQA-CHAT 2023 and MEDIQA-SUM 2023 challenges:
TRAIN: 67
VALID: 20
TEST1: 40 ( MEDIQA-CHAT TASK B test set )
TEST2: 40 ( MEDIQA-CHAT TASK C test set )
TEST3: 40 ( MEDIQA-SUM TASK C test set )
Speaker Tagging: Some ACI-BENCH subsets include swapped speaker tags (e.g., [patient], [doctor]) that originate from the Automatic Speech Recognition (ASR) process. This reflects a known and common limitation of ASR-based transcripts, which we intentionally left uncorrected, as our goal was to provide a realistic dataset that captures such errors. Automated methods (including LLM-based approaches, such as Max's method) can be used to identify and correct these speaker-tagging issues.
The data here is published under a Creative Commons Attribution 4.0 International License (CC BY): https://creativecommons.org/licenses/by/4.0/
Please cite our Nature Scientific Data paper if you use the full dataset or any subset of ACI-BENCH.
- Asma Ben abacha (abenabacha at microsoft dot com)
- Wen-wai Yim (yimwenwai at microsoft dot com)
This repository contains the data and source code for:
"ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation". Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Neal Snider, Thomas Lin, Meliha Yetisgen. Nature Scientific Data, 2023.
Paper: https://www.nature.com/articles/s41597-023-02487-3
Dataset: https://figshare.com/articles/dataset/aci-bench-corpus_zip/22494601?file=41498793
@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}
}
The ACI-BENCH collection consists of full doctor-patient conversations and associated clinical notes and includes the data splits from the MEDIQA-CHAT 2023 and MEDIQA-SUM 2023 challenges:
TRAIN: 67
VALID: 20
TEST1: 40 ( MEDIQA-CHAT TASK B test set )
TEST2: 40 ( MEDIQA-CHAT TASK C test set )
TEST3: 40 ( MEDIQA-SUM TASK C test set )
Speaker Tagging: Some ACI-BENCH subsets include swapped speaker tags (e.g., [patient], [doctor]) that originate from the Automatic Speech Recognition (ASR) process. This reflects a known and common limitation of ASR-based transcripts, which we intentionally left uncorrected, as our goal was to provide a realistic dataset that captures such errors. Automated methods (including LLM-based approaches, such as Max's method) can be used to identify and correct these speaker-tagging issues.
The data here is published under a Creative Commons Attribution 4.0 International License (CC BY): https://creativecommons.org/licenses/by/4.0/
Please cite our Nature Scientific Data paper if you use the full dataset or any subset of ACI-BENCH.
- Asma Ben abacha (abenabacha at microsoft dot com)
- Wen-wai Yim (yimwenwai at microsoft dot com)