
# Automatic Glossary of Clinical Terminology (v2023)
This dataset contains 422,070 short, computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. To do so, we prompted the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept.

![figure-method-green-3.png](https://s3.amazonaws.com/moonup/production/uploads/5f04e8865d08220171a0ad3f/qg1oijewMvUOGdn1C79Bs.png)

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## Quality Control


**IMPORTANT:** Following a quality control, we report that the definitions include a majority of factual, insightful, and fluent definitions. However, about 30% of the definitions generated by this procedure do not meet the high standards required for presentation to users, or for usage by machine learning models in scenarios requiring reasoning, due to their imperfect quality. However, more than 95% of the definitions appear useful for biomedical model pre-training. We therefore release this dataset for building retrieval-based systems, and evaluate large biomedical language models on the definition-generation task (and eventually for low-rank finetuning of existing language models).
<br clear="all" />

## License
The license for this work is subject to both [SnomedCT](https://www.nlm.nih.gov/healthit/snomedct/snomed_licensing.html) and [OpenAI API](https://openai.com/policies/terms-of-use) agreements. We strongly recommend checking those licenses before making use of this dataset.

## Citation
If you use this dataset, please cite the following work: [AGCT @ BioNLP 2023](https://aclanthology.org/2023.bionlp-1.23/)
```
@inproceedings{remy-etal-2023-automatic,
    title = "Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge",
    author = "Remy, Fran{\c{c}}ois  and
      Demuynck, Kris  and
      Demeester, Thomas",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.23",
    doi = "10.18653/v1/2023.bionlp-1.23",
    pages = "265--272",
    abstract = "Background: More than 400.000 biomedical concepts and some of their relationships are contained in SnomedCT, a comprehensive biomedical ontology. However, their concept names are not always readily interpretable by non-experts, or patients looking at their own electronic health records (EHR). Clear definitions or descriptions in understandable language or often not available. Therefore, generating human-readable definitions for biomedical concepts might help make the information they encode more accessible and understandable to a wider public. Objective: In this article, we introduce the Automatic Glossary of Clinical Terminology (AGCT), a large-scale biomedical dictionary of clinical concepts generated using high-quality information extracted from the biomedical knowledge contained in SnomedCT.Methods: We generate a novel definition for every SnomedCT concept, after prompting the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept. A significant subset of the generated definitions was subsequently evaluated by NLP researchers with biomedical expertise on 5-point scales along the following three axes: factuality, insight, and fluency. Results: AGCT contains 422,070 computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. The average length of the definitions is 49 words. The definitions were assigned average scores of over 4.5 out of 5 on all three axes, indicating a majority of factual, insightful, and fluent definitions. Conclusion: AGCT is a novel and valuable resource for biomedical tasks that require human-readable definitions for SnomedCT concepts. It can also serve as a base for developing robust biomedical retrieval models or other applications that leverage natural language understanding of biomedical knowledge.",
}
```
size_categories:100K<n<1M language:en license:other biology medical region:us
{"language": ["en"], "license": "other", "size_categories": ["100K<n<1M"], "pretty_name": "Automatic Glossary of Clinical Terminology (v2023)", "tags": ["biology", "medical"]}
TAGS
#size_categories-100K<n<1M #language-English #license-other #biology #medical #region-us 


# Automatic Glossary of Clinical Terminology (v2023)
This dataset contains 422,070 short, computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. To do so, we prompted the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept.

!URL

<div class="not-prose">
<img align="right" alt="URL" src="URL width="256" />

</div>

## Quality Control


IMPORTANT: Following a quality control, we report that the definitions include a majority of factual, insightful, and fluent definitions. However, about 30% of the definitions generated by this procedure do not meet the high standards required for presentation to users, or for usage by machine learning models in scenarios requiring reasoning, due to their imperfect quality. However, more than 95% of the definitions appear useful for biomedical model pre-training. We therefore release this dataset for building retrieval-based systems, and evaluate large biomedical language models on the definition-generation task (and eventually for low-rank finetuning of existing language models).
<br clear="all" />

## License
The license for this work is subject to both SnomedCT and OpenAI API agreements. We strongly recommend checking those licenses before making use of this dataset.

If you use this dataset, please cite the following work: AGCT @ BioNLP 2023

# Automatic Glossary of Clinical Terminology (v2023)
This dataset contains 422,070 short, computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. To do so, we prompted the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept.

!URL

<div class="not-prose">
<img align="right" alt="URL" src="URL width="256" />

</div> ## Quality Control


IMPORTANT: Following a quality control, we report that the definitions include a majority of factual, insightful, and fluent definitions. However, about 30% of the definitions generated by this procedure do not meet the high standards required for presentation to users, or for usage by machine learning models in scenarios requiring reasoning, due to their imperfect quality. However, more than 95% of the definitions appear useful for biomedical model pre-training. We therefore release this dataset for building retrieval-based systems, and evaluate large biomedical language models on the definition-generation task (and eventually for low-rank finetuning of existing language models).
<br clear="all" /> ## License
The license for this work is subject to both SnomedCT and OpenAI API agreements. We strongly recommend checking those licenses before making use of this dataset.

If you use this dataset, please cite the following work: AGCT @ BioNLP 2023
TAGS
#size_categories-100K<n<1M #language-English #license-other #biology #medical #region-us 
 # Automatic Glossary of Clinical Terminology (v2023)
This dataset contains 422,070 short, computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. To do so, we prompted the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept.

!URL

<div class="not-prose">
<img align="right" alt="URL" src="URL width="256" />

</div> ## Quality Control


IMPORTANT: Following a quality control, we report that the definitions include a majority of factual, insightful, and fluent definitions. However, about 30% of the definitions generated by this procedure do not meet the high standards required for presentation to users, or for usage by machine learning models in scenarios requiring reasoning, due to their imperfect quality. However, more than 95% of the definitions appear useful for biomedical model pre-training. We therefore release this dataset for building retrieval-based systems, and evaluate large biomedical language models on the definition-generation task (and eventually for low-rank finetuning of existing language models).
<br clear="all" /> ## License
The license for this work is subject to both SnomedCT and OpenAI API agreements. We strongly recommend checking those licenses before making use of this dataset.

If you use this dataset, please cite the following work: AGCT @ BioNLP 2023
passage: TAGS
#size_categories-100K<n<1M #language-English #license-other #biology #medical #region-us 
# Automatic Glossary of Clinical Terminology (v2023)
This dataset contains 422,070 short, computer-generated definitions for SnomedCT concepts, covering various domains such as diseases, procedures, drugs, and anatomy. To do so, we prompted the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality verbalization of the SnomedCT relationships of the to-be-defined concept.

!URL

<div class="not-prose">
<img align="right" alt="URL" src="URL width="256" />

</div>## Quality Control


IMPORTANT: Following a quality control, we report that the definitions include a majority of factual, insightful, and fluent definitions. However, about 30% of the definitions generated by this procedure do not meet the high standards required for presentation to users, or for usage by machine learning models in scenarios requiring reasoning, due to their imperfect quality. However, more than 95% of the definitions appear useful for biomedical model pre-training. We therefore release this dataset for building retrieval-based systems, and evaluate large biomedical language models on the definition-generation task (and eventually for low-rank finetuning of existing language models).
<br clear="all" />## License
The license for this work is subject to both SnomedCT and OpenAI API agreements. We strongly recommend checking those licenses before making use of this dataset.

If you use this dataset, please cite the following work: AGCT @ BioNLP 2023