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Expand CDA to NCIT Mappings for 12 tissues & Enhance OpUberonMapper #79
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…t term prediction
…t term prediction.
great @rajdeepmondal-el ! Maybe we can review tomorrow? |
cc: @pnrobinson |
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@rajdeepmondal-el these mappings seem very good
Manually checking these, I did spot 1-2 that probably need updating, e.g. this one should probably be NCIT:C3749 Alveolar Rhabdomyosarcoma and not NCIT:C194245 Malignant Neoplasm of Connective and Soft Tissue of Head, Face and Neck
Maybe @pnrobinson, you and I could manually review some more of these today on the call?
Thanks a lot for pointing it out, Agree with you @justaddcoffee I can explain why the result is that so, and some probable ways to make the predictions even more accurate. For this specific example, the primary_diagnosis_site consists of quite a lot of spurious information that might make the model a bit confused, it is trying to be more context-aware than necessary, which I can solve by assigning weights to the primary diagnosis part. Also, there are some edge cases which i will also share. |
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handing this off to @ielis
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Hi @rajdeepmondal-el
thanks a lot for your excellent work!
I plugged the mapping files into the library and tested to run one of the driver script and it looks OK.
It's looking good, please feel free to merge the PR!
Thank you very much @ielis, I have merged it. |
This introduces a significant expansion to our CDA to NCIT mappings, featuring a comprehensive addition of twelve CSV files for 12 tissues for CDA to NCIT mappings. These files cover a diverse range of tissues: bone, brain, breast, cervix, colon, heart, kidney, liver, lung, pancreas, skin, and thyroid. Please find these detailed mappings organized in
src/oncoexporter/ncit_mapping_files/cda_to_ncit_tissue_wise_mappings
.Furthermore,
OpUberonMapper
was updated withinsrc/oncoexporter/cda/mapper/op_uberon_mapper.py
, adding new terms and mappings that translate the string representations of anatomical locations into their corrosponding UBERON terms.Kindly please take a look,
Thanks a lot,
Rajdeep