back

by brandonb·10y ago·view on hn ↗
Very cool. As you're thinking about unsupervised or semi-supervised deep learning, consider medical data sets as a potential domain.

ImageNet has 1,034,908 labeled images. In a hospital setting, you'd be lucky to get 1000 participants.

That means those datasets really show off the power of unsupervised, semi-supervised, or one-shot learning algorithms. And if you set up the problem well, each increment of ROC translates into a life saved.

Happy to point you in the right direction when the time comes—my email is in my HN profile.

1 comments
Most top Hospitals in USA have high quality data on Millions of patients the legal and bureaucratic challenges to sharing those datasets are insurmountable. However if you are affiliated a university hospital its not difficult to get 690,000 CT scans or time series data with 400+ signals from 450,000 Operations.

Even outcomes data procedures performed and diagnosis across multiple visits can be easily obtained for millions of patients on national scale. My research involves applying deep learning to these datasets.

Isn't the labeling really tricky, though?

In my limited experience, EHRs aren't usually setup to handle structured labeling of something like an image. There are lots of different fields for text entry that can be unstructured. Then the only label left is the billing code, which ends up being a poor choice of label since the hospital often bills for what it can get reimbursed for, not what you actually had.

You don't need labels for the image if you can get them from other patient information, in particular, the diagnosis.

E.g. you know from image metadata that it's a chest x-ray of patient #1234 at 2012/03/04. Then you automatically check patient EHR near that date - do they have lung cancer Y/N; do they have broken ribs Y/N; do they have TB Y/N, etc, and make your image labels based on that. How diagnosis are codified, though, differs significantly between various medical systems, I have no idea how it's in USA EHR.

Very cool. This is an academic project? Can you talk at all about the tools you're using?
Yes its an academic project. You can find more info on : http://www.computationalhealthcare.com

We are using data provided AHRQ HCUP and some internal datasets. TensorFlow for ML.