task_categories:token-classification task_ids:named-entity-recognition annotations_creators:machine-generated language_creators:machine-generated multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:de bio biomedical medical clinical arxiv:2208.14493 region:us
GPTNERMED is a novel open synthesized dataset and neural named-entity-recognition (NER) model for German texts in medical natural language processing (NLP).
@article{FREI2023104478,
title = {Annotated dataset creation through large language models for non-english medical NLP},
journal = {Journal of Biomedical Informatics},
volume = {145},
pages = {104478},
year = {2023},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2023.104478},
url = {https://www.sciencedirect.com/science/article/pii/S1532046423001995},
author = {Johann Frei and Frank Kramer},
keywords = {Natural language processing, Information extraction, Named entity recognition, Data augmentation, Knowledge distillation, Medication detection},
abstract = {Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processing (NLP). In general, developing and applying new NLP pipelines in domain-specific contexts for tasks often requires custom-designed datasets to address NLP tasks in a supervised machine learning fashion. When operating in non-English languages for medical data processing, this exposes several minor and major, interconnected problems such as the lack of task-matching datasets as well as task-specific pre-trained models. In our work, we suggest to leverage pre-trained large language models for training data acquisition in order to retrieve sufficiently large datasets for training smaller and more efficient models for use-case-specific tasks. To demonstrate the effectiveness of your approach, we create a custom dataset that we use to train a medical NER model for German texts, GPTNERMED, yet our method remains language-independent in principle. Our obtained dataset as well as our pre-trained models are publicly available at https://github.com/frankkramer-lab/GPTNERMED.}
}
---
annotations_creators:
- machine-generated
language:
- de
language_creators:
- machine-generated
license: []
multilinguality:
- monolingual
pretty_name: GPTNERMED
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- bio
- biomedical
- medical
- clinical
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# GPTNERMED Dataset for German medical NER entities

## Dataset Description

- **Repository:** https://github.com/frankkramer-lab/GPTNERMED
- **Paper:** https://doi.org/10.1016/j.jbi.2023.104478
- **ArXiv-Preprint:** https://arxiv.org/abs/2208.14493

## Dataset Summary

This dataset contains the synthetic German sentences with annotated entities (`Medikation`, `Dosis`, `Diagnose`) from the GPTNERMED project.

The sentences as well as the annotations are **not** manually validated by medical professionals and therefore this dataset is **no** gold standard dataset.

The dataset consists of 9,845 sentences (121,027 tokens by SpaCy Tokenizer, 245,107 tokens by the GPT tokenizer) with the following labels:
| Label | Count | #Tokens (SpaCy) |
| --- | --- | -- |
| Medikation | 9868 | 10138 |
| Dosis | 7547 | 15845 |
| Diagnose | 5996 | 7656 |

## Dataset Structure
The train/test/dev-split (80%, 10%, 10%) of the data loader is as follows:\
`<--     train: 0.8     --><--  test: 0.1  --><--  dev: 0.1  -->`\
The splits are selected arbitrarily as the dataloader requires a split configuration. All sample sentences are however homogeneous in origin and splits could also be performed otherwise.

Every sample is a sentence with its text (property `sentence`) and its corresponding NER labels (property `ner_labels` / List of labels).\
Every NER label entry has a char-wise start and stop index (property `start`, `stop`) and a label class (property `ner_class`).

### Citation Information

If you like our work, cite our paper and give us a star on GitHub.\
(See the links above)
