bvanaken commited on
Commit
05a9d67
1 Parent(s): 63df844

Create README

Browse files
Files changed (1) hide show
  1. README.md +66 -0
README.md ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: "en"
3
+ tags:
4
+ - bert
5
+ - medical
6
+ - clinical
7
+ - mortality
8
+ thumbnail: "https://core.app.datexis.com/static/paper.png"
9
+ ---
10
+
11
+ # CORe Model - Clinical Mortality Risk Prediction
12
+
13
+ ## Model description
14
+
15
+ 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).
16
+ 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.
17
+
18
+ This model checkpoint is **fine-tuned on the task of mortality risk prediction**.
19
+ The model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.
20
+
21
+ #### How to use CORe Diagnosis Prediction
22
+
23
+ You can load the model via the transformers library:
24
+ ```
25
+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
26
+ tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-mortality-prediction")
27
+ model = AutoModelForSequenceClassification.from_pretrained("bvanaken/CORe-clinical-mortality-prediction")
28
+ ```
29
+
30
+ The following code shows an inference example:
31
+
32
+ ```
33
+ 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."
34
+
35
+ tokenized_input = tokenizer(input, return_tensors="pt")
36
+ output = model(**tokenized_input)
37
+
38
+ import torch
39
+ predictions = torch.softmax(output.logits.detach(), dim=1)
40
+ mortality_risk_prediction = predictions[0][1].item()
41
+ ```
42
+
43
+
44
+ ### More Information
45
+
46
+ For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
47
+
48
+ ### Cite
49
+
50
+ ```bibtex
51
+ @inproceedings{vanaken21,
52
+ author = {Betty van Aken and
53
+ Jens-Michalis Papaioannou and
54
+ Manuel Mayrdorfer and
55
+ Klemens Budde and
56
+ Felix A. Gers and
57
+ Alexander Löser},
58
+ title = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
59
+ Knowledge Integration},
60
+ booktitle = {Proceedings of the 16th Conference of the European Chapter of the
61
+ Association for Computational Linguistics: Main Volume, {EACL} 2021,
62
+ Online, April 19 - 23, 2021},
63
+ publisher = {Association for Computational Linguistics},
64
+ year = {2021},
65
+ }
66
+ ```