Commit 81fd0a95 authored by Chia Ying Chiu's avatar Chia Ying Chiu
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Update NLP_Project_Code.ipynb

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NLP project ideas:
I found this paper, which seems to be what I was proposing! Labeling clinical notes to map the ICD codes (Multi-label text classification) https://arxiv.org/abs/2102.09136
Another article used the same data set MIMIC-III to evaluate the ICD9 code assignment of RNNs and CNNs. https://github.com/lsy3/clinical-notes-diagnosis-dl-nlp
Article used the same data set MIMIC-III to evaluate the ICD9 code assignment of RNNs and CNNs. https://github.com/lsy3/clinical-notes-diagnosis-dl-nlp
GitHub seems to provide code and cleaned data sets.
Paper: https://arxiv.org/pdf/1802.02311v2.pdf
This one used a different dataset to assign ICD-10 code with BERTS: http://ceur-ws.org/Vol-2380/paper_67.pdf
MIMIC-III: https://paperswithcode.com/dataset/mimic-iii
Link to data:
MIMIC- III: https://uab.box.com/s/pjf41j05n33wjktu93p98vla3mfvhjwu
MIMICII: https://uab.box.com/s/5pfa044uywvuwq084798w52fvdjtw8l3
Cantemist: https://uab.box.com/s/l0bm5v3mg814j4iv4uqv01cy74kff24e
Using the resources from the GitHub project to assign ICD9 code using different multi-label text classification models
Is it possible to optimize their models?
Prediction models using the ICD9 codes with covariates (insurance type, gender*, ethnicity, marital status, admission type) to see what are the top ICD codes that are associated with prolonged length of stay. https://towardsdatascience.com/predicting-hospital-length-of-stay-at-time-of-admission-55dfdfe69598
Compare the prediction models of different multi-label text classification models, and see if the results are agreed across models
| Task | Assigned To | Deadline|
------|-------------|----------
| |
| Run the CNN and RNN models, refer to the GitHub link above| Zaid & Chia | 11/15|
|------|
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``` python
```
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