@dlrubin, Here’s my model how to use unsupervised deep learning for medical image combined with structured clinical data to predict clinical outcome.
I’m trying to convert medical image into small-size vector representing the the original image, and combine it with other clinical features.
I uploaded the code to [github][1] for building encoder for medical image, and make feature vector from medical image based on radiology CDM. This is totally compatible with other OHDSI tool ecosystem, such as FeatureExtraction package and PatientLevelPrediction package.
The details of model architecture and the package for extracting and converting metadata from DICOM to R-CDM file are available at: https://github.com/NEONKID/RCDM-ETL
What a wonderful amount of work you’ve done! I would love to find out more about what you are doing with it. I couldn’t get to your poster at the symposium and the posters aren’t available on the OHDSI website yet.
Radiology imaging is classified into Procedure in OMOP-CDM, to my understanding.
I argue that the domain of LOINC concepts imported from RadLex should be classified into ‘Procedure’ rather than ‘Measurement’. You can find the list of LOINC codes in LOINC/RSNA code book here.
I think this (adoption of LOINC/RadLex as standard vocabulary for radiologic imaging procedure) may facilitate further standardization of radiology images in OMOP-CDM and collaboration between clinical data scientists and radiologists in OHDSI.
I agree with @SCYou. @Polina_Talapova@zhuk@Alexdavv
are you agree with that as well?
Can you take a look at the technical aspect of how to implement this change?
Just reviewed the list.
My understanding was that the most of these concepts should be used with the result what isn’t possible in the Procedure Domain.
But now I see most of them are complex procedures and cannot be described by the categorical result.
