I'm building a healthcare NLP model and need de-identified clinical text datasets. What are the best options?

For de-identified clinical text, the best options depend on whether you need real clinical notes, biomedical text, or synthetic/near-clinical data. Here are the strongest choices:

Best real-world de-identified clinical text datasets

1) MIMIC-III / MIMIC-IV

• What it is: Large de-identified ICU/EHR dataset from Beth Israel Deaconess.
• Text available: Clinical notes, discharge summaries, radiology reports, nursing notes, etc.
• Why it’s popular: One of the most widely used datasets for clinical NLP.
• Access: Requires credentialing, data use agreement, and CITI training.
• Best for: Clinical note classification, concept extraction, summarization, phenotyping, temporal modeling.

MIMIC-IV note module is especially useful if you want more modern data.

2) i2b2 / n2c2 shared task datasets

• What it is: De-identified clinical notes released for specific NLP challenges.
• Text available: Notes annotated for entities like problems, meds, allergies, smoking, temporal relations, etc.
• Why it’s useful: High-quality annotations for supervised learning and evaluation.
• Access: Usually requires application/DUA, sometimes limited distribution.
• Best for: Named entity recognition, relation extraction, de-identification, concept normalization.

3) THYME corpus

• What it is: De-identified clinical narratives, often focused on temporal information.
• Best for: Temporal relation extraction, event timelines, clinical sequence modeling.
• Access: Usually via restricted access agreements.

4) ShARe/CLEF eHealth datasets

• What it is: Clinical text and annotated disorders/entities from shared tasks.
• Best for: Entity recognition, normalization, semantic indexing.

5) FAERS / public adverse event reports

• What it is: Not full clinical notes, but free-text-ish safety reports and case narratives.
• Best for: Pharmacovigilance, adverse event extraction.
• Caveat: Not a substitute for rich clinical notes.

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Best publicly accessible or easier-to-access alternatives

6) PubMed / PMC Open Access

• What it is: Biomedical articles, abstracts, full texts in some cases.
• Why it helps: Fully public and large-scale.
• Best for: Pretraining biomedical language models, terminology extraction, relation extraction.
• Caveat: Not clinical notes; style differs from EHR text.

7) MedNLI

• What it is: Natural language inference dataset based on clinical sentences.
• Best for: Clinical language understanding, entailment tasks.
• Caveat: Not raw notes.

8) RadGraph / MIMIC-CXR reports

• What it is: Radiology report text with structured annotations.
• Best for: Radiology NLP, entity/relation extraction, report generation.
• Access: MIMIC-CXR is restricted but widely used; RadGraph provides annotations.

9) CORD-19 / other health-related corpora

• What it is: Research literature, not EHR text.
• Best for: Biomedical QA, summarization, literature mining.

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Synthetic options

If you need something safer/easier for prototyping:

10) Synthetic clinical notes

• Generated from templates, simulators, or LLM-based pipelines.
• Pros: No PHI concerns, easy to share internally.
• Cons: May not capture real-world noise and complexity.
• Best for: Early experimentation, pipeline testing, schema development.

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My recommendation by use case

• General clinical NLP on real notes:
  MIMIC-IV note module first, then i2b2/n2c2 for specific tasks.

• Entity extraction / normalization:
  i2b2/n2c2, ShARe/CLEF, MIMIC-III/IV notes.

• Temporal reasoning:
  THYME, some i2b2 temporal tasks.

• Radiology:
  MIMIC-CXR, RadGraph.

• Model pretraining:
  MIMIC notes + PubMed/PMC Open Access together is a strong combo.

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Practical cautions

• Even “de-identified” clinical text may still have re-identification risk, so check:
  • Data use agreements
  • IRB requirements
  • Allowed model training / sharing constraints
  • Whether generated outputs could memorize PHI
• Avoid mixing datasets with incompatible licenses or distribution terms.

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If you want the shortest answer

The best starting point is usually:

1. MIMIC-IV notes for large-scale real de-identified clinical text
2. i2b2/n2c2 for annotated task-specific datasets
3. PubMed/PMC Open Access for additional biomedical pretraining data

If you want, I can also give you:

• a dataset comparison table,
• a “best dataset by task” shortlist, or
• the exact access steps for MIMIC and i2b2/n2c2.