JMasr/balidea-snomed-dataset

CU2 SNOMED Benchmark

Dataset Summary

This dataset packages the public portion of the CU2 benchmark for Spanish clinical text and SNOMED CT coding.

Public tasks:

• mention_to_snomed
• document_to_snomed

Public splits:

• train
• validation

Hidden from the public dataset:

• benchmark test gold labels

Corpus Profile

• Unique SNOMED codes across the processed corpus: 8128
• Unique documents: 1000
• mention_to_snomed rows: 26480
• document_to_snomed rows: 1000

The benchmark has a long-tail label distribution in both tasks:

• mention_to_snomed: 4686 codes appear once; 7291 appear at most 5 times; the top 10 codes account for 8.08% of all mention-level code assignments
• document_to_snomed: 5148 codes appear once; 7461 appear at most 5 times; the top 10 codes account for 6.86% of all document-level code assignments

Mention surface forms are short on average but varied:

• Unique mention surface forms: 15741
• Average words per mention surface form: 3.23
• Median words per mention surface form: 2.0

Most common mention lengths:

• 1 word(s): annotations=8345, unique_surface_forms=2040
• 2 word(s): annotations=6846, unique_surface_forms=3708
• 3 word(s): annotations=3023, unique_surface_forms=2284
• 4 word(s): annotations=2311, unique_surface_forms=1993
• 5 word(s): annotations=1740, unique_surface_forms=1598
• 6 word(s): annotations=1192, unique_surface_forms=1148
• 7 word(s): annotations=826, unique_surface_forms=806
• 8 word(s): annotations=629, unique_surface_forms=612
• 9 word(s): annotations=426, unique_surface_forms=418
• 10 word(s): annotations=299, unique_surface_forms=297

Tasks

mention_to_snomed

Strict span-and-code prediction over localized mentions.

Fields:

• doc_id
• mention_text
• start_char
• end_char
• gold_snomed_codes

Primary metrics:

• precision_strict
• recall_strict
• f1_strict

document_to_snomed

Document-level SNOMED coding as a set prediction task.

Fields:

• doc_id
• text
• gold_snomed_codes

Primary metrics:

• precision_micro
• recall_micro
• f1_micro

Secondary metric:

• subset_accuracy

Split Policy

• Official raw test remains hidden and is not included in this public HF export.
• Official raw train is partitioned deterministically into benchmark train and validation.
• Validation derivation rule: Derived deterministically from official train by doc_id using md5(doc_id) % 100 < 10.

Public Export Contents

• document_to_snomed: train=658, validation=92
• mention_to_snomed: train=15527, validation=2193

Processed Dataset Statistics

mention_to_snomed

• Full processed rows: 26480
• Unique documents: 1000
• Unique SNOMED codes: 8128
• Mean codes per row: 1.000
• Median codes per row: 1.0

Split coverage across the processed benchmark:

• train: rows=15527, docs=658, unique_codes=5622
• validation: rows=2193, docs=92, unique_codes=1345
• test: rows=8760, docs=250, unique_codes=3803

Most frequent mention-level codes:

• 5880005: count=291 share=1.10%
• 387713003: count=281 share=1.06%
• 64882008: count=281 share=1.06%
• 22253000: count=203 share=0.77%
• 300848003: count=201 share=0.76%
• 77477000: count=198 share=0.75%
• 84387000: count=186 share=0.70%
• 417163006: count=185 share=0.70%
• 252416005: count=165 share=0.62%
• 419099009: count=149 share=0.56%

document_to_snomed

• Full processed rows: 1000
• Unique documents: 1000
• Unique SNOMED codes: 8128
• Mean codes per row: 22.298
• Median codes per row: 19.0

Split coverage across the processed benchmark:

• train: rows=658, docs=658, unique_codes=5622
• validation: rows=92, docs=92, unique_codes=1345
• test: rows=250, docs=250, unique_codes=3803

Most frequent document-level codes:

• 5880005: count=247 share=1.11%
• 64882008: count=188 share=0.84%
• 387713003: count=167 share=0.75%
• 84387000: count=165 share=0.74%
• 22253000: count=133 share=0.60%
• 300848003: count=131 share=0.59%
• 419099009: count=130 share=0.58%
• 252416005: count=128 share=0.57%
• 77477000: count=122 share=0.55%
• 38341003: count=118 share=0.53%

Source Provenance

The benchmark is built from:

• DisTEMIST
• SympTEMIST
• MedProcNER

Current source contribution in the processed benchmark:

• distemist: 7431 mention records across 832 documents
• medprocner: 8193 mention records across 499 documents
• symptemist: 10856 mention records across 990 documents

All three resources come from the Barcelona Supercomputing Center TEMU shared-task ecosystem and are fused into benchmark task schemas after deterministic normalization.

Licensing Notes

• SympTEMIST local package includes CC BY 4.0
• MedProcNER local package includes CC BY 4.0
• DisTEMIST provenance is tracked from the local distemist_zenodo package root; verify redistribution terms from the original distribution before public push if required by your release workflow

Hidden Test Policy

This public dataset intentionally excludes benchmark test gold labels. Use the repository's private/local evaluation workflow for real leaderboard scoring on hidden test predictions.

Limitations

• The public HF dataset is intended for reproducible training and validation, not for blind final scoring.
• Source corpora share the same underlying document collection in many cases; benchmark split logic is handled at document level.
• Strict mention scoring requires exact span and exact SNOMED code-set agreement.