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RadLex and Standardization of ontology for radiology procedures


(Seng Chan You) #21

@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.

I know i need more explanation to show entire process. I can show it in the poster at the OHDSI symposium.
[1]: https://github.com/OHDSI/StudyProtocolSandbox/blob/master/radiologyCovariateExtraction/R/RadiologyCovariateExtraction.R


(Seng Chan You) #22

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


(Andrew Williams) #23

This would be useful for work we are doing and have planned.


(Andrew Williams) #24

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.


(Seng Chan You) #25

I need to bring this up again. @christian_reich @dymshyts

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.


(Dmytry Dymshyts) #26

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?


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