This dataset contains structured data extracted from technical notes produced by the e-NatJus system (Núcleo de Apoio Técnico do Judiciário), a Brazilian judicial support program that provides evidence-based technical advice on health technologies in litigation contexts.

The e-NatJus program assists judges in making informed decisions about healthcare-related lawsuits by providing technical assessments of medicines, procedures, and health products requested through the Brazilian judicial system.

Dataset Summary

Total Records: ~500,000 technical notes
File Size: ~539 MB (Parquet format)
Language: Portuguese (pt-BR)
Format: Apache Parquet
Encoding: UTF-8
Columns: 65 structured fields

Supported Tasks

Healthcare technology assessment analysis
Medical-legal text classification
Evidence-based medicine research
Healthcare policy analysis
Drug and procedure recommendation systems
Judicial decision support systems

Dataset Structure

Data Fields

The dataset contains 65 fields organized into the following categories:

1. Identification and Metadata

titulo (str): Technical note title
idNotaTecnica (str): Unique identifier
arquivo (str): Source HTML filename
status (str): Processing status

2. Patient Data

txtIdade (str): Patient age
selStaGenero (str): Gender (m/f)
txtCidade (str): City/municipality

3. Legal Representative

txtNomeAdvogado (str): Attorney name
txtNumeroOABAdvogado (str): Bar association number
selDefensoriaPublica (str): Public defender/prosecutor indicator

4. Procedural Data

selEsfera (str): Judicial sphere (state/federal)
txtServentia (str): Court/jurisdiction

5. Clinical Assessment

txtCid (str): ICD-10 diagnosis code
txtDescAvaliacaoDiagnosticoSemCID (str): Diagnosis description
txaMeioDiagRealizado (str): Diagnostic procedures performed

6. Technology Type

selTipoTecnologia (str): Technology type
- "1": Medicine
- "2": Procedure
- "3": Product

7. Medicine-Specific Fields (when type = "1")

txtDcb (str): Active pharmaceutical ingredient
txtDcbComercial (str): Commercial name
txtViaAdministracao (str): Route of administration
txaPosologia (str): Dosage/posology

8. Procedure-Specific Fields (when type = "2")

txtProcedimento (str): Procedure description

9. Product-Specific Fields (when type = "3")

txtProduto (str): Health product description

10. Regulatory Status (medicines and products only)

selRegistroAnvisa (str): ANVISA registration (S/N)
selSituacaoAnvisa (str): Registration status (A=Valid, I=Expired)

11. SUS Availability

selDisponivelSus (str): Available in public health system (S/N/B/X)
selTabelaTecnologia (str): Incorporation table
- "R": RENAME (National Essential Medicines)
- "M": REMUME (Municipal Medicines)
- "S": SIGTAP (Procedures Table)
- "C": CIB Deliberation
- "N": None

12. Cost Information (mainly for medicines)

txtLaboratorio (str): Manufacturer
txtMarcaComercial (str): Commercial brand
txtApresentacao (str): Packaging/presentation
txtPrecoFabrica (str): Factory price
txtPrecoMaximoGoverno (str): Maximum government price
txtPrecoMaximoConsumidor (str): Maximum consumer price

13. Evidence and Technical Foundation

txaEficaciaSeguranca (str): Efficacy and safety analysis
txaImpactoTecnologia (str): Technology impact assessment
selRecomendacaoConitec (str): CONITEC recommendation (F/D/V)
selEvidenciaCientifica (str): Scientific evidence indicator (S/N/B)
txaReferencia (str): Bibliographic references

14. Conclusion and Opinion

selConclusao (str): Final opinion (F=Favorable, N=Unfavorable)
txaConclusao (str): Full conclusion text
selAlegacaoUrgencia (str): Urgency allegation (S/N)

15. Responsible Parties

txtNatResponsavel (str): Technical responsible
txtInstituicaoResponsavel (str): Responsible institution
selApoioTutoria (str): Tutoring support indicator (S/N)
origem_natjus (str): e-NatJus origin/nucleus

16. Status and Dates

data_emissao (str): Emission date and time (DD/MM/YYYY HH:MM:SS)
status_tecnologia (str): Technology status

17. Attachments

anexos_count (int): Number of attachments (0-4)
anexos_info (str): JSON with attachment details
anexo_filename, anexo2_filename, anexo3_filename, anexo4_filename (str): Individual filenames
anexo_hash, anexo2_hash, anexo3_hash, anexo4_hash (str): Download hashes
anexo_download_url, anexo2_download_url, anexo3_download_url, anexo4_download_url (str): Download URLs

Data Conventions

The dataset uses the following conventions for missing or non-applicable values:

String value: Field filled with actual content
"NÃO_PREENCHIDO": Field exists but is empty/blank
"NÃO_APLICÁVEL": Field does not apply to the specific technology type
None: Parsing error or HTML element not found

Conditional Fields

Some fields are only applicable under certain conditions:

selSituacaoAnvisa: Only when selRegistroAnvisa = "S" (has registration)
selTabelaTecnologia: Only when selDisponivelSus = "S" (available in SUS)
Medicine-specific fields: Only when selTipoTecnologia = "1"
Procedure-specific fields: Only when selTipoTecnologia = "2"
Product-specific fields: Only when selTipoTecnologia = "3"

Data Loading

Using Polars (Recommended)

import polars as pl

# Lazy loading (memory efficient)
df = pl.scan_parquet("base_enatjus.parquet")

# Filter medicines only
medicamentos = df.filter(pl.col("selTipoTecnologia") == "1").collect()

# Count by technology type
tipo_counts = df.group_by("selTipoTecnologia").agg(
    pl.count().alias("count")
).collect()

Using Pandas

import pandas as pd

# Load full dataset
df = pd.read_parquet("base_enatjus.parquet")

# Load with column selection (memory efficient)
df = pd.read_parquet(
    "base_enatjus.parquet",
    columns=["idNotaTecnica", "txtDcb", "selConclusao"]
)

Using DuckDB

import duckdb

# Query without loading into memory
result = duckdb.query("""
    SELECT selTipoTecnologia, COUNT(*) as total
    FROM 'base_enatjus.parquet'
    GROUP BY selTipoTecnologia
""").to_df()

Data Quality

Filtered content: Files with error messages were excluded:
- "Permissão negada" (Permission denied)
- "Nota Técnica não encontrada" (Technical note not found)
- "É necessário estar logado" (Login required)
Minimum size: Files under 1000 characters were excluded
Encoding: All files processed with UTF-8 encoding
Parser: BeautifulSoup with 'html.parser'

Use Cases

Healthcare Technology Assessment: Analyze patterns in clinical evidence and recommendations
Pharmaceutical Research: Study medicine prescriptions, dosages, and availability
Legal Analytics: Understand judicial health litigation patterns
Evidence-Based Medicine: Research correlation between scientific evidence and recommendations
Health Economics: Analyze pricing data and cost-effectiveness
Public Health Policy: Assess SUS availability and incorporation of technologies
NLP Applications: Train models for medical-legal text understanding

Limitations

Data represents Brazilian healthcare and legal context
Some fields may be incomplete due to original data entry practices
Monetary values are stored as strings and may require parsing
Attachment files are not included (only metadata)
Temporal coverage varies (check data_emissao field)

Citation

If you use this dataset in your research, please cite:

@dataset{enatjus_v2_2025,
  title={e-NatJus Technical Notes Dataset v2},
  author={Brazilian Judicial Technical Support Program},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/BrunoDCDO/enatjus_v2}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

You are free to:

Share — copy and redistribute the material
Adapt — remix, transform, and build upon the material

Under the following terms:

Attribution — You must give appropriate credit

Contact

For questions or issues regarding this dataset, please open an issue on the Hugging Face dataset page.

Additional Resources

Full Codebook - Detailed documentation of all fields
Validation Report - Data quality validation
e-NatJus Official Website: https://www.cnj.jus.br/programas-e-acoes/e-natjus/

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