Create README

README.md

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+---
+language: "en"
+tags:
+- bert
+- medical
+- clinical
+- diagnosis
+thumbnail: "https://core.app.datexis.com/static/paper.png"
+---
+
+# CORe Model - Clinical Diagnosis Prediction
+
+## Model description
+
+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).
+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 diagnosis prediction**.
+The model expects patient admission notes as input and outputs multi-label ICD9-code predictions.
+
+#### Model Predictions
+The model makes predictions on a total of 9237 labels. These contain 3- and 4-digit ICD9 codes and textual descriptions of these codes. The 4-digit codes and textual descriptions help to incorporate further topical and hierarchical information into the model during training (see Section 4.2 _ICD+: Incorporation of ICD Hierarchy_ in our paper). We recommend to only use the **3-digit code predictions at inference time**, because only those have been evaluated in our work.
+
+#### How to use CORe Diagnosis Prediction
+
+You can load the model via the transformers library:
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-diagnosis-prediction")
+model = AutoModelForSequenceClassification.from_pretrained("bvanaken/CORe-clinical-diagnosis-prediction")
+```
+
+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.sigmoid(output.logits)
+predicted_labels = [model.config.id2label[_id] for _id in (predictions > 0.3).nonzero()[:, 1].tolist()]
+```
+Note: For the best performance, we recommend to determine the thresholds (0.3 in this example) individually per label.
+
+
+### More Information
+
+For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
+
+### Cite
+
+```bibtex
+@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},
+}
+```