xMEN is an extensible toolkit for Cross-lingual (x) Medical Entity Normalization. Through its compatibility with the BigBIO (BigScience Biomedical) framework, it can be used out-of-the box to run experiments with many open biomedical datasets. It can also be easily integrated with existing Named Entity Recognition (NER) pipelines.
xMEN is available through PyPi:
pip install xmen
We use Poetry for building, testing and dependency management (see pyproject.toml).
A very simple pipeline highlighting the main components of xMEN can be found in notebooks/Getting_Started.ipynb
Usually, BigBIO-compatible datasets can just be loaded from the Hugging Face Hub:
from datasets import load_dataset
dataset = load_dataset("distemist", "distemist_linking_bigbio_kb")
To use xMEN with existing NER pipelines, you can also create a dataset at runtime.
from xmen.data import from_spacy
docs = ... # list of spaCy docs with entity spans
dataset = from_spacy(docs)
xMEN provides a convenient command line interface to prepare entity linking pipelines by creating target dictionaries and pre-computing indices to link to concepts in them.
Run xmen help to get an overview of the available commands.
Configuration is done through .yaml files. For examples, see the conf folder.
Run xmen dict to create dictionaries to link against. Although the most common use case is to create subsets of the UMLS, it also supports passing custom parser scripts for non-UMLS dictionaries.
Note: Creating UMLS subsets requires a local installation of the UMLS metathesaurus (not only MRCONSO.RRF). In the examples, we assume that the environment variable $UMLS_HOME points to the installation path. You can either set this variable, or replace the path with your local installation.
Example configuration for Medmentions:
name: medmentions
dict:
umls:
lang:
- en
meta_path: ${oc.env:UMLS_HOME}/2017AA/META
version: 2017AA
semantic_types:
- T005
- T007
- T017
- T022
- T031
- T033
- T037
- T038
- T058
- T062
- T074
- T082
- T091
- T092
- T097
- T098
- T103
- T168
- T170
- T201
- T204
sabs:
- CPT
- FMA
- GO
- HGNC
- HPO
- ICD10
- ICD10CM
- ICD9CM
- MDR
- MSH
- MTH
- NCBI
- NCI
- NDDF
- NDFRT
- OMIM
- RXNORM
- SNOMEDCT_US
Running xmen --dict conf/medmentions.yaml creates a .jsonl file from the described UMLS subset.
Parsing scripts for custom dictionaries can be provided with the --code option (examples can be found in the dicts folder).
Example configuration for DisTEMIST:
name: distemist
dict:
custom:
lang:
- es
distemist_path: path/to/dictionary_distemist.tsv
Running xmen dict conf/distemist.yaml --code dicts/distemist.py --key distemist_gazetteer creates a .jsonl file from the custom DisTEMIST gazetteer.
The xmen index command is used to compute term indices from a dictionary created through the dict command.
If an index already exists, you will be prompted to overwrite the existing file (or pass --overwrite).
xMEN provides implementations of different neural and non-neural candidate generators
Based on the implementation from scispaCy.
YAML file:
linker:
candidate_generation:
ngram: ~
Run xmen index my_config.yaml --ngram or xmen index my_config.yaml --all to create the index.
To use the linker at runtime, pass the index folder as an argument:
from xmen.linkers import TFIDFNGramLinker
ngram_linker = TFIDFNGramLinker(index_base_path="/path/to/my/index/ngram", k=100)
predictions = ngram_linker.predict_batch(dataset)
Example usage: see notebooks/BioASQ_DisTEMIST.ipynb
Dense Retrieval based on SapBERT embeddings.
YAML file:
linker:
candidate_generation:
sapbert:
model_name: cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR
Run xmen index my_config.yaml --sapbert or xmen index my_config.yaml --all to create the FAISS index.
To use the linker at runtime, pass the index folder as an argument. To make predictions on a batch of documents, you have to pass a batch size, as the SapBERT linker runs on the GPU by default:
from xmen.linkers import SapBERTLinker
sapbert_linker = SapBERTLinker(
index_base_path = "/path/to/my/index/sapbert",
k = 1000
)
predictions = sapbert_linker.predict_batch(dataset, batch_size=128)
If you have loaded a yaml-config as a dictionary-like object, you may also just pass it as kwargs:
sapbert_linker = SapBERTLinker(**config)
By default, SapBERT assumes a CUDA device is available. If you want to disable cuda, pass cuda=False to the constructor.
Example usage: see notebooks/BioASQ_DisTEMIST.ipynb
Different candidate generators often work well for different kinds of entity mentions, and it can be helpful to combine their predictions.
In xMEN, this can be easily achieved with an EnsembleLinker:
from xmen.linkers import EnsembleLinker
ensemble_linker = EnsembleLinker()
ensemble_linker.add_linker('sapbert', sapbert_linker, k=10)
ensemble_linker.add_linker('ngram', ngram_linker, k=10)
You can call predict_batch on the EnsembleLinker just as with any other linker.
Sometimes, you want to compare the ensemble performance to individual linkers and already have the candidate lists. To avoid recomputation, you can use the reuse_preds argument:
prediction = ensemble_linker.predict_batch(dataset, 128, 100, reuse_preds={'sapbert' : predictions_sap, 'ngram' : predictions_ngram'})
Note: reuse_preds currently does not support Hugging Face DatasetDict objects, so you would have to call it on each split individually.
Example usage: see notebooks/BioASQ_DisTEMIST.ipynb
When labelled training data is available, a trainable reranker can improve ranking of candidate lists a lot.
To train a cross-encoder, first create a dataset of mention / candidate pairs:
from xmen.reranking.cross_encoder import CrossEncoderReranker, CrossEncoderTrainingArgs
from xmen.kb import load_kb
# Load a KB from a pre-computed dictionary (jsonl) to obtain synonyms for concept encoding
kb = load_kb('path/to/my/dictionary.jsonl')
# Obtain prediction from candidate generator (see above)
candidates = linker.predict_batch(dataset)
cross_enc_ds = CrossEncoderReranker.prepare_data(candidates, dataset, kb)
Then you can use this dataset to train a supervised reranking model:
from xmen.reranking.cross_encoder import CrossEncoderReranker, CrossEncoderTrainingArgs
# Number of epochs to train
n_epochs = 10
# Any BERT model, potentially language-specific
cross_encoder_model = 'bert-base-multilingual-cased'
args = CrossEncoderTrainingArgs(n_epochs, cross_encoder_model)
rr = CrossEncoderReranker()
# Fit the model
rr.fit(args, cross_enc_ds['train'].dataset, cross_enc_ds['validation'].dataset)
# Predict on test set
prediction = rr.rerank_batch(candidates['test'], cross_enc_ds['test'])
Example usage: see notebooks/BioASQ_DisTEMIST.ipynb
TODO
We support various optional components for transforming input data and result sets:
- Sampling
- Abbrevation expansion
- Filtering by UMLS semantic groups
- Filtering by UMLS semantic types
- Replacement of retired CUIS
xMEN provides implementations of common entity linking metrics (e.g., a wrapper for neleval)
Example usage: see notebooks/BioASQ_DisTEMIST.ipynb
TODO