Comments (4)
Hi Xianlong,
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Actually I'm currently working on it.
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I've used the synthetic data from medGAN to train a heart-failure prediction model (I supplemented the dataset with synthetic heart-failure case patients, as they are rarer compared to control patients), and I've observed an improved recall. But this was a very preliminary work, and more rigorous evaluation is necessary.
from medgan.
Hi Ed,
Thanks for the reply!
For 2. Have you try to train the model entirely on the synthetic data? if the model which performs well on the synthetic data can also performs well on the real data (kind of like training and validation sets), that I think will be a strong argument that synthetic data is really good, am I right?
Also, as you mentioned heart-failure prediction model, I was wondering are you also generating the label of the EHR data? For example, heart-failure will be 1 and control will be 0 (or say can this model be used to generated labeled data? Like adding the label as the last column of the data.)
Thank you
from medgan.
Hi Xianlong,
Figure 3 and 7 in my paper is exactly what you described. I trained logistic regression classifiers with both real and synthetic data, then tested them on held-out real data. There are many details that cannot be covered here, so I recommend you read my paper.
You can generate labeled dataset in many ways. You can add an additional column like you suggested. Or you can develop a conditional generator. In my case, I trained two separate medGANs, one for case dataset, the other for control dataset. But as I said, this experiment was not rigorously conducted, so I can't say that my method is optimal.
Thanks,
Ed
from medgan.
cool! I didn't see the connection between these two at the beginning.
I think that will be very useful if we can train models without accessing the real data set. I will look into this direction.
Thanks!
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Related Issues (20)
- Other fields in data generation HOT 5
- Synthetic Image HOT 7
- Generating different features HOT 13
- Error with numpy 1.16.3 HOT 4
- Variable Misnomer outlayer of Generator
- Distribution of ICD Codes for generated patients HOT 2
- Input / Output dimensions differ HOT 2
- Restoring of trained model HOT 2
- outputs-0.data-00000-of-00001: DATA_LOSS: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator? HOT 1
- Trained Model HOT 1
- MedGAN on PyTorch? HOT 1
- Training Losses and optimal parameters
- Why the google records project has been archived? what if i Still want to contribute?
- REGARDING DATA SOURCE
- How to use code on own dataset
- How to interpret the samples? HOT 11
- keep_prob always 1.0? HOT 1
- All generatorDims must be equal to embeddingDim? HOT 1
- Dimensions of input data HOT 1
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