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Hi @Rafii
Thank you! I started this to help people who need these models and tools but never get to have them to make a difference in this world.

Can you share like a few rows to see what we are dealing with here? Like is it patient history, diagnosis, what's each row represent. Then, we'll see what we can extract from it or how it can be used.

Sure @MaziyarPanahi

, its a bit long,

So the features are demographics , clinical and Phylogenetic tree,
'age',
'sex',
'ethnicity',
'cigs_perday',
'years_smoking',
'packyears',
'smoking_status_merged',
'is.family.lung',
'ECOG_PS',
'pathologyTNM',
'pT_stage_per_patient',
'pN_stage_per_patient',
'LVI_per_patient',
'PL_per_patient',
'margin_status_per_patient',
'size_pathology_per_patient',
'Surgery_type',
'histology_lesion1',
'histology_lesion1_merged',
'lesion1_sampled',
'histology_lesion2',
'lesion2_sampled',
'histology_multi_full',
'histology_multi_full_genomically.confirmed',
'LUAD_pred_subtype'
'overall_survival' ---- Target Variable (number of days)

values are like,
0 32 68 0 8 20 35 35 0 1 0 1 2 0 1 40 2 3 1 6 2 0 0 0 1 3 0 19 12 41 2 4 0 1849 0 1849 0 1849 0 1849 6 3 1
1 107 81 1 7 44.5 49 109.03 0 0 0 3 4 0 1 75 2 4 1 6 2 7 6 8 1 3 0 19 12 41 2 0 1 1362 1 1362 1 1362 0 1362 6 3 0
2 109 60 1 7 20 38 38 2 0 0 1 2 0 1 36 2 3 1 6 2 0 0 0 0 0 4 17 11 40 0 1 1 2224 1 2224 1 1935 0 2224 5 3 0

I hope ts understandable.

thank you for responding

Thanks @Rafii for sharing these! I actually love to use those as entities, generate datasets and train NER models so it might help you where you can extract what's needed from the text via those models

Thanks for the response, I think I was not clear enough, my dataset is like the following, in structured tabular format , my target is to predict a SURVIVAL in number of days, I believe NER might not be applicable here, your take?
Age: 67
Sex: Male
Histology: Adenocarcinoma
TNM_Stage: T2aN0M0
Smoking_packs : 4,