Core Clinical Mortality Prediction

🚀 CORe Model - Clinical Mortality Risk Prediction

The CORe model is designed for clinical mortality risk prediction, leveraging self - supervised knowledge integration on clinical data.

🚀 Quick Start

The CORe (Clinical Outcome Representations) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self - Supervised Knowledge Integration. 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.

This model checkpoint is fine - tuned on the task of mortality risk prediction. The model expects patient admission notes as input and outputs the predicted risk of in - hospital mortality.

💻 Usage Examples

Basic Usage

You can load the model via the transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-mortality-prediction")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/CORe-clinical-mortality-prediction")

Advanced Usage

The following code shows an inference example:

input = "CHIEF COMPLAINT: Headaches\n\nPRESENT ILLNESS: 58yo man w/ hx of hypertension, AFib on coumadin presented to ED with the worst headache of his life."

tokenized_input = tokenizer(input, return_tensors="pt")
output = model(**tokenized_input)

import torch
predictions = torch.softmax(output.logits.detach(), dim=1)
mortality_risk_prediction = predictions[0][1].item()

📚 Documentation

For all the details about CORe and contact info, please visit CORe.app.datexis.com.

📄 License

Cite

@inproceedings{vanaken21,
  author    = {Betty van Aken and
               Jens - Michalis Papaioannou and
               Manuel Mayrdorfer and
               Klemens Budde and
               Felix A. Gers and
               Alexander Löser},
  title     = {Clinical Outcome Prediction from Admission Notes using Self - Supervised
               Knowledge Integration},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the
               Association for Computational Linguistics: Main Volume, {EACL} 2021,
               Online, April 19 - 23, 2021},
  publisher = {Association for Computational Linguistics},
  year      = {2021},
}