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NLP_Project_Code.ipynb

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+    "NLP project ideas: \n",
+    "\n",
+    "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 \n",
+    "\n",
+    "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 \n",
+    "\n",
+    "GitHub seems to provide code and cleaned data sets.  \n",
+    "\n",
+    " Paper: https://arxiv.org/pdf/1802.02311v2.pdf \n",
+    "\n",
+    "This one used a different dataset to assign ICD-10 code with BERTS: http://ceur-ws.org/Vol-2380/paper_67.pdf \n",
+    "\n",
+    " \n",
+    "\n",
+    "MIMIC-III: https://paperswithcode.com/dataset/mimic-iii \n",
+    "\n",
+    "Link to data: \n",
+    "\n",
+    "MIMIC- III: https://uab.box.com/s/pjf41j05n33wjktu93p98vla3mfvhjwu \n",
+    "\n",
+    "MIMICII: https://uab.box.com/s/5pfa044uywvuwq084798w52fvdjtw8l3 \n",
+    "\n",
+    "Cantemist: https://uab.box.com/s/l0bm5v3mg814j4iv4uqv01cy74kff24e \n",
+    "\n",
+    " \n",
+    "\n",
+    "Using the resources from the GitHub project to assign ICD9 code using different multi-label text classification models  \n",
+    "\n",
+    "Is it possible to optimize their models? \n",
+    "\n",
+    "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 \n",
+    "\n",
+    "Compare the prediction models of different multi-label text classification models, and see if the results are agreed across models \n",
+    "\n",
+    " \n",
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