This a UMCU/CardioBERTa.nl_clinical base model finetuned for span classification. For this model we used IOB-tagging. Using the IOB-tagging schema facilitates the aggregation of predictions over sequences. This specific model is trained on a batch of 240 span-labeled documents.

Expected input and output

The input should be a string with Dutch cardio clinical text.

CardioNER model --medication is a muticlass span classification model. The classes that can be predicted are ['medication'].

Extracting span classification from CardioNER model --medication

The following script converts a string of <512 tokens to a list of span predictions.

python

from transformers import pipeline

le_pipe = pipeline('ner',
                    model=model,
                    tokenizer=model, aggregation_strategy="simple",
                    device=-1)

named_ents = le_pipe(SOME_TEXT)

To process a string of arbitrary length you can split the string into sentences or paragraphs using e.g. pysbd or spacy(sentencizer) and iteratively parse the list of with the span-classification pipe.

Alternatively you might try

named_ents = le_pipe(SOME_TEXT, stride=256)

Data description

50/50 Train/validation split on CardioCCC, a manually labeled cardiology corpus

Acknowledgement

This is part of the DT4H project.

Doi and reference

For more details about training/eval and other scripts, see CardioNER github repo. and for more information on the background, see Datatools4Heart Huggingface/Website

DT4H-IE/CardioBERTa.nl_clinical_NL_MED

Model Card for Cardioner Model --Medication

Expected input and output

Extracting span classification from CardioNER model --medication

Data description

Acknowledgement

Doi and reference

DT4H-IE/
CardioBERTa.nl_clinical_NL_MED