belgian-deduce 4.0.0

Rule-based de-identification for Belgian clinical text

Belgian Deduce

belgian_deduce is a rule-based de-identification package for Belgian clinical text. It ships as a standalone Python package, uses Belgian lookup data and defaults, and is built on top of docdeid.

• Remove names, places, institutions, dates, ages, identifiers, phone numbers, e-mail addresses, and URLs from Belgian medical text
• Tune behavior through config, lookup structures, and custom processors
• Use Belgian defaults for postal codes, phone numbers, and national register numbers

belgian_deduce started from the original deduce project. This repository now maintains its own package identity, configuration, documentation, and Belgian-specific defaults.

De-identification is never perfect. Validate and adapt the package on your own data before using it in a critical environment.

Citing

If you use belgian_deduce, cite the original DEDUCE paper for the underlying method and reference this repository and version in your implementation notes:

Menger, V.J., Scheepers, F., van Wijk, L.M., Spruit, M. (2017). DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text, Telematics and Informatics, 2017, ISSN 0736-5853

Installation

Install the latest release from PyPI:

pip install belgian-deduce

For unreleased changes, install directly from GitHub:

pip install "git+https://github.com/stighellemans/belgian-deduce.git"

For reproducible environments, pin to a commit:

pip install "git+https://github.com/stighellemans/belgian-deduce.git@<commit-sha>"

Getting Started

from belgian_deduce import Deduce

model = Deduce()

text = (
    "betreft: Jan Janssens, rijksregisternummer 85.07.30-033.28, patnr 000334433. "
    "De patient J. Janssens is 64 jaar oud en woont in Leuven. Hij werd op "
    "10 oktober 2018 door arts Peter de Smet ontslagen uit UZ Leuven. "
    "Voor nazorg kan hij worden bereikt via [e-mail] of "
    "0470 12 34 56."
)

doc = model.deidentify(text)
print(doc.deidentified_text)

betreft: [PERSON-1], rijksregisternummer [NATIONAL_REGISTER_NUMBER-1], patnr [ID-1]. De patient [PERSON-1] is [AGE-1] jaar oud en woont in [LOCATION-1]. Hij werd op [DATE-1] door arts [PERSON-2] ontslagen uit [HOSPITAL-1]. Voor nazorg kan hij worden bereikt via [EMAIL-1] of [PHONE_NUMBER-1].

If patient metadata is known, pass it explicitly:

from belgian_deduce import Deduce, Person

model = Deduce()
patient = Person(first_names=["Jan"], initials="JJ", surname="Janssens")
doc = model.deidentify(text, metadata={"patient": patient})

Metadata can also be used for more than the primary patient. The pipeline supports:

• persons: one or more additional Person objects treated as regular people
• addresses: one or more Address objects that should be tagged as location
• entities: arbitrary exact metadata matches via MetadataEntity
• Person.birth_date, Person.aliases, and Person.addresses

from datetime import date

from belgian_deduce import Address, Deduce, MetadataEntity, Person

deduce = Deduce()

metadata = {
    "patient": Person(
        first_names=["Jan"],
        surname="Jansen",
        birth_date=date(1980, 3, 12),
        addresses=[
            Address(
                street="Kerkstraat",
                house_number="12A",
                postal_code="9000",
                city="Gent",
            )
        ],
    ),
    "persons": [
        Person(
            first_names=["Peter"],
            surname="de Visser",
            aliases=["Dr. Peter de Visser"],
        )
    ],
    "entities": [
        MetadataEntity(text="UZ Gent", tag="hospital"),
        MetadataEntity(text="ABC-12345", tag="id"),
    ],
}

doc = deduce.deidentify(text, metadata=metadata)

French-speaking notes can be handled through the same API. A practical path is to provide metadata for names, birth dates, addresses, and institutions:

from datetime import date

from belgian_deduce import Address, Deduce, MetadataEntity, Person

deduce = Deduce()

text = (
    "Patient Jean Dupont, né le 12 mars 1980, habite Rue de la Loi 12, "
    "1000 Bruxelles. Sophie Martin consulte à Hôpital Erasme."
)

metadata = {
    "patient": Person(
        first_names=["Jean"],
        surname="Dupont",
        birth_date=date(1980, 3, 12),
        addresses=[
            Address(
                street="Rue de la Loi",
                house_number="12",
                postal_code="1000",
                city="Bruxelles",
            )
        ],
    ),
    "persons": [Person(first_names=["Sophie"], surname="Martin")],
    "entities": [MetadataEntity(text="Hôpital Erasme", tag="hospital")],
}

doc = deduce.deidentify(text, metadata=metadata)
print(doc.deidentified_text)

Patient [PATIENT], né le [DATE-1], habite [LOCATION-1]. [PERSON-1] consulte à [HOSPITAL-1].

Documentation

The project documentation lives in docs/source/tutorial.md and docs/source/migrating.md.

Contributing

Contribution guidance is available in CONTRIBUTING.md.

Versions

• 4.0.0 - First standalone belgian_deduce release with Belgian defaults and independent docs/tooling
• Earlier entries in CHANGELOG.md predate the standalone release and are preserved for provenance

Authors

• Vincent Menger - original DEDUCE implementation
• Stig Hellemans - Belgian standalone package and maintenance

License

This project remains licensed under LGPL-3.0-or-later. See LICENSE.md.