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https://github.com/vmenger/deduce

Deduce: de-identification method for Dutch medical text
https://github.com/vmenger/deduce

deidentification dutch dutch-clinical-nlp information-extraction nlp python python-library text-mining text-processing

Last synced: about 2 months ago
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Deduce: de-identification method for Dutch medical text

Host: GitHub
URL: https://github.com/vmenger/deduce
Owner: vmenger
License: gpl-3.0
Created: 2017-08-25T11:26:34.000Z (almost 7 years ago)
Default Branch: main
Last Pushed: 2024-04-29T15:21:17.000Z (2 months ago)
Last Synced: 2024-05-03T19:09:09.741Z (about 2 months ago)
Topics: deidentification, dutch, dutch-clinical-nlp, information-extraction, nlp, python, python-library, text-mining, text-processing
Language: Python
Homepage:
Size: 7.21 MB
Stars: 47
Watchers: 3
Forks: 18
Open Issues: 10
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md

Lists

awesome-dutch-nlp - Deduce: de-identification method for Dutch medical text - identification method for Dutch medical text (Uncategorized / Uncategorized)
awesome-pii - Deduce - identification method for Dutch medical text (Tools / Text)

README

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# deduce

> Deduce 3.0.0 is out! It is way more accurate, and faster too. It's fully backward compatible, but some functionality is scheduled for removal, read more about it here: [docs/migrating-to-v3](https://deduce.readthedocs.io/en/latest/migrating.html)

* :sparkles: Remove sensitive information from clinical text written in Dutch

* :mag: Rule based logic for detecting e.g. names, locations, institutions, identifiers, phone numbers

* :triangular_ruler: Useful out of the box, but customization higly recommended

* :seedling: Originally validated in [Menger et al. (2017)](http://www.sciencedirect.com/science/article/pii/S0736585316307365), but further optimized since

> :exclamation: Deduce is useful out of the box, but please validate and customize on your own data before using it in a critical environment. Remember that de-identification is almost never perfect, and that clinical text often contains other specific details that can link it to a specific person. Be aware that de-identification should primarily be viewed as a way to mitigate risk of identification, rather than a way to obtain anonymous data.

Currently, `deduce` can remove the following types of Protected Health Information (PHI):

* :bust_in_silhouette: person names, including prefixes and initials

* :earth_americas: geographical locations smaller than a country

* :hospital: names of hospitals and healthcare institutions

* :calendar: dates (combinations of day, month and year)

* :birthday: ages

* :1234: BSN numbers

* :1234: identifiers (7+ digits without a specific format, e.g. patient identifiers, AGB, BIG)

* :phone: phone numbers

* :e-mail: e-mail addresses 

* :link: URLs

## Citing

If you use `deduce`, please cite the following paper:  

[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](http://www.sciencedirect.com/science/article/pii/S0736585316307365)

## Installation

``` python

pip install deduce

```

## Getting started

The basic way to use `deduce`, is to pass text to the `deidentify` method of a `Deduce` object:

```python

from deduce import Deduce

deduce = Deduce()

text = (

    "betreft: Jan Jansen, bsn 111222333, patnr 000334433. De patient J. Jansen is 64 jaar oud en woonachtig in "

    "Utrecht. Hij werd op 10 oktober 2018 door arts Peter de Visser ontslagen van de kliniek van het UMCU. "

    "Voor nazorg kan hij worden bereikt via [email protected] of (06)12345678."

)

doc = deduce.deidentify(text)

```

The output is available in the `Document` object:

```python

from pprint import pprint

pprint(doc.annotations)

AnnotationSet({

    Annotation(text="(06)12345678", start_char=272, end_char=284, tag="telefoonnummer"),

    Annotation(text="111222333", start_char=25, end_char=34, tag="bsn"),

    Annotation(text="Peter de Visser", start_char=153, end_char=168, tag="persoon"),

    Annotation(text="[email protected]", start_char=247, end_char=268, tag="email"),

    Annotation(text="patient J. Jansen", start_char=56, end_char=73, tag="patient"),

    Annotation(text="Jan Jansen", start_char=9, end_char=19, tag="patient"),

    Annotation(text="10 oktober 2018", start_char=127, end_char=142, tag="datum"),

    Annotation(text="64", start_char=77, end_char=79, tag="leeftijd"),

    Annotation(text="000334433", start_char=42, end_char=51, tag="id"),

    Annotation(text="Utrecht", start_char=106, end_char=113, tag="locatie"),

    Annotation(text="UMCU", start_char=202, end_char=206, tag="instelling"),

})

print(doc.deidentified_text)

"""betreft: [PERSOON-1], bsn [BSN-1], patnr [ID-1]. De [PERSOON-1] is [LEEFTIJD-1] jaar oud en woonachtig in 

[LOCATIE-1]. Hij werd op [DATUM-1] door arts [PERSOON-2] ontslagen van de kliniek van het [INSTELLING-1]. 

Voor nazorg kan hij worden bereikt via [EMAIL-1] of [TELEFOONNUMMER-1]."""

```

Additionally, if the names of the patient are known, they may be added as `metadata`, where they will be picked up by `deduce`:

```python

from deduce.person import Person

patient = Person(first_names=["Jan"], initials="JJ", surname="Jansen")

doc = deduce.deidentify(text, metadata={'patient': patient})

print (doc.deidentified_text)

"""betreft: [PATIENT], bsn [BSN-1], patnr [ID-1]. De [PATIENT] is [LEEFTIJD-1] jaar oud en woonachtig in 

[LOCATIE-1]. Hij werd op [DATUM-1] door arts [PERSOON-2] ontslagen van de kliniek van het [INSTELLING-1]. 

Voor nazorg kan hij worden bereikt via [EMAIL-1] of [TELEFOONNUMMER-1]."""

```

As you can see, adding known names keeps references to `[PATIENT]` in text. It also increases recall, as not all known names are contained in the lookup lists. 

## Versions

For most cases the latest version is suitable, but some specific milestones are:

* `3.0.0` - Many optimizations in accuracy, smaller refactors, further speedups

* `2.0.0` - Major refactor, with speedups, many new options for customizing, functionally very similar to original 

* `1.0.8` - Small bugfixes compared to original release

* `1.0.1` - Original release with [Menger et al. (2017)](http://www.sciencedirect.com/science/article/pii/S0736585316307365)

Detailed versioning information is accessible in the [changelog](CHANGELOG.md). 

## Documentation

All documentation, including a more extensive tutorial on using, configuring and modifying `deduce`, and its API, is available at: [docs/tutorial](https://deduce.readthedocs.io/en/latest/) 

## Contributing

For setting up the dev environment and contributing guidelines, see: [docs/contributing](https://deduce.readthedocs.io/en/latest/contributing.html)

## Authors

* **Vincent Menger** - *Initial work* 

* **Jonathan de Bruin** - *Code review*

* **Pablo Mosteiro** - *Bug fixes, structured annotations*

## License

This project is licensed under the GNU General Public License v3.0 - see the [LICENSE.md](LICENSE.md) file for details