language:
- de
license: other
size_categories:
- n<1K
task_categories:
- text-classification
- feature-extraction
pretty_name: UroLlmEvalSet
dataset_info:
features:
- name: text
dtype: string
- name: label
list:
- name: date
dtype: string
- name: diagnosis
dtype: string
splits:
- name: eval
num_bytes: 69884
num_examples: 149
download_size: 29639
dataset_size: 69884
configs:
- config_name: default
data_files:
- split: eval
path: data/eval-*
tags:
- medical
UroLlmEvalSet
Content
This dataset contains 149 short textual descriptions in German medical language about the diagnosis and medical history of patients with prostate cancer. The prostate cancer diagnosis is not present in all of the texts and many texts also contain other tumor diagnoses. The texts are labeled with the three-character ICD-10 codes of the diagnoses and with the date of the initial diagnosis of the tumor. Some diagnosis dates in the text are specified only as years, while others include both year and month. In the labels, the dates are therefore strings of the format "YYYY" or "YYYY-MM". If there is no clearly attributable first diagnosis date for a tumor diagnosis in a text, the corresponding date value is "0". The dataset schema is as follows:
{
'text': string, # Text about diagnosis and current medical history of a patient
'label': [ # Multiple labels (diagnosis/date pairs) may be present for the text.
{
'diagnosis': string # ICD-code of tumor diagnosis
'date': string # Date of initial diagnosis of this tumor
}
]
}
Loading the data set
import datasets
datasets.load_dataset("stefan-m-lenz/UroLlmEvalSet", split="eval")
Benchmarking LLMs for tumor documentation
This dataset was created for a benchmark to evaluate LLMs for tumor documentation.
The code for running the UroLlmEval benchmark can be found here:
https://github.com/stefan-m-lenz/UroLlmEval
License
The data is released under terms and conditions defined in the data usage LICENSE. As this dataset is primarily intended for evaluation purposes, the license restricts its distribution to prevent an undisclosed inclusion in LLM training data.
Citation and further information
Further information about the dataset and about the benchmark can be found in the following preprint article:
@article{UroLlmEval_2025,
title = {Can open source large language models be used for tumor documentation in {Germany}? - {An} evaluation on urological doctors' notes},
author = {Lenz, Stefan and Ustjanzew, Arsenij and Jeray, Marco and Panholzer, Torsten},
year = {2025},
month = {Jan},
journal = {arXiv preprint},
volume = {arXiv:2501.12106},
doi = {10.48550/arXiv.2501.12106},
url = {http://arxiv.org/abs/2501.12106},
publisher = {arXiv}
}
If you use this dataset in your work, please consider citing the article.