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The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
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It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
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#### How to use
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You can load the model via the transformers library:
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```
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model = AutoModel.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
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```
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```bibtex
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@inproceedings{vanaken21,
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The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
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It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
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#### How to use CORe
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You can load the model via the transformers library:
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```
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model = AutoModel.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
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```
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### Pre-Training Data
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The model is based on [BioBERT](https://huggingface.co/dmis-lab/biobert-v1.1) pre-trained on PubMed data.
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The _Clinical Outcome Pre-Training_ included discharge summaries from the MIMIC III training set (specified [here](https://github.com/bvanaken/clinical-outcome-prediction/blob/master/tasks/mimic_train.csv)), medical transcriptions from [MTSamples](https://mtsamples.com/) and clinical notes from the i2b2 challenges 2006-2012. It further includes ~10k case reports from PubMed Central (PMC), disease articles from Wikipedia and article sections from the [MedQuAd](https://github.com/abachaa/MedQuAD) dataset extracted from NIH websites.
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### More Information
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For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
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### Cite
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```bibtex
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@inproceedings{vanaken21,
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