A pytorch GPU implementation of #016095 al. "Diagnosis Neural Collaborative Filtering for Accurate Medical Recommendation" of Qualification.
* python==3.6
* pandas==0.24.2
* numpy==1.16.2
* pytorch==1.0.1
* gensim==3.7.1
* tensorboardX==1.6 (mainly useful when you want to visulize the loss, see https://github.com/lanpa/tensorboard-pytorch)
python main.py
We provide two processed datasets: MIMIC3-20 and MIMIC3-30
train.rating:
- Train file.
- Each Line is a training instance: ICD9_new\t ITEM_new\t COUNT_new (if have)
test.rating:
- Test file (positive instances).
- Each Line is a testing instance: ICD9_new\t ITEM_new\t COUNT_new (if have)
test.negative
- Test file (negative instances).
- Each line corresponds to the line of test.rating, containing 99 negative samples.
- Each line is in the format: (ICD9_new,ITEM_new)\t negativeItemID1\t negativeItemID2 ...
The trained model weights are under "models/"