## Dutch Biomedical Entity Linking

### Summary
- RoBERTa-based basemodel that is trained from scratch on Dutch hospital notes ([medRoBERTa.nl](https://huggingface.co/CLTL/MedRoBERTa.nl)).
- 2nd-phase pretrained using [self-alignment](https://doi.org/10.48550/arXiv.2010.11784) on UMLS-derived Dutch biomedical ontology.
- fine-tuned on automatically generated weakly labelled corpus from Wikipedia.
- evaluation results on [Mantra GSC](https://doi.org/10.1093/jamia/ocv037) corpus can be found in the [report](https://github.com/fonshartendorp/dutch_biomedical_entity_linking/blob/main/report/report.pdf)


All code for generating the training data, training the model and evaluating it, can be found in the [github](https://github.com/fonshartendorp/dutch_biomedical_entity_linking) repository.

### Usage

The following script (reused the original [sapBERT repository](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext?text=kidney)) computes the embeddings for a list of input entities (strings)

```
import numpy as np
import torch
from tqdm.auto import tqdm
from transformers import AutoTokenizer, AutoModel  

tokenizer = AutoTokenizer.from_pretrained("fonshartendorp/dutch_biomedical_entity_linking)")  
model = AutoModel.from_pretrained("fonshartendorp/dutch_biomedical_entity_linking").cuda()

# replace with your own list of entity names
dutch_biomedical_entities = ["versnelde ademhaling", "Coronavirus infectie", "aandachtstekort/hyperactiviteitstoornis", "hartaanval"]

bs = 128 # batch size during inference
all_embs = []
for i in tqdm(np.arange(0, len(dutch_biomedical_entities), bs)):
    toks = tokenizer.batch_encode_plus(dutch_biomedical_entities[i:i+bs], 
                                       padding="max_length", 
                                       max_length=25, 
                                       truncation=True,
                                       return_tensors="pt")
    toks_cuda = {}
    for k,v in toks.items():
        toks_cuda[k] = v.cuda()
    cls_rep = model(**toks_cuda)[0][:,0,:] # use CLS representation as the embedding
    all_embs.append(cls_rep.cpu().detach().numpy())

all_embs = np.concatenate(all_embs, axis=0)
```

For (Dutch) biomedical entity linking, the following steps should be performed:

1. Request UMLS (and SNOMED NL) license
2. Precompute embeddings for all entities in the UMLS with the fine-tuned model
3. Compute embedding of the new, unseen mention with the fine-tuned model
4. Perform nearest-neighbour search (or search FAISS-index) for linking the embedding of the new mention to its most similar embedding from the UMLS

{"language": ["nl"], "tags": ["Biomedical entity linking", "sapBERT", "bioNLP", "embeddings", "representation learning"]}
transformers pytorch tf roberta feature-extraction Biomedical entity linking sapBERT bioNLP embeddings representation learning nl endpoints_compatible region:us