clinlp 0.1.1

Performant and production-ready NLP pipelines for clinical text written in Dutch

clinlp

• :hospital: clinical + :netherlands: nl + :clipboard: NLP = :sparkles: clinlp
• :star: Performant and production-ready NLP pipelines for clinical text written in Dutch
• :rocket: Open source, created and maintained by the Dutch Clinical NLP community
• :triangular_ruler: Useful out of the box, but customization highly recommended

Read the principles and goals, futher down :arrow_down:

Contact and contributing

clinlp is very much still being shaped, so if you are enthusiastic about using or contributing to clinlp, please don't hesitate to get in touch (email | issue). We would be very happy to discuss your ideas and needs, whether its from the perspective of an (end) user, engineer or clinician, and formulate a roadmap with next steps together.

Getting started

Installation

pip install clinlp

Example

import clinlp
import spacy

nlp = spacy.blank("clinlp")

# Sentences
nlp.add_pipe('clinlp_sentencizer')

# Entities
ruler = nlp.add_pipe('entity_ruler')

terms = {
    'covid_19_symptomen': [
        'verkoudheid', 'neusverkoudheid', 'loopneus', 'niezen', 'vermoeidheid',
        'keelpijn', 'hoesten', 'benauwdheid', 'kortademigheid', 'verhoging', 
        'koorts', 'verlies van reuk', 'verlies van smaak'
    ]
}

for term_description, terms in terms.items():
    ruler.add_patterns([{'label': term_description, 'pattern': term} for term in terms])
import clinlp
import spacy

nlp = spacy.blank("clinlp")

# Sentences
nlp.add_pipe('clinlp_sentencizer')

# Entities
ruler = nlp.add_pipe('entity_ruler')

terms = {
    'covid_19_symptomen': [
        'verkoudheid', 'neusverkoudheid', 'loopneus', 'niezen', 'vermoeidheid',
        'keelpijn', 'hoesten', 'benauwdheid', 'kortademigheid', 'verhoging', 
        'koorts', 'verlies van reuk', 'verlies van smaak'
    ]
}

for term_description, terms in terms.items():
    ruler.add_patterns([{'label': term_description, 'pattern': term} for term in terms])

# Qualifiers
nlp.add_pipe('clinlp_context_matcher')

text = (
    "Patiente bij mij gezien op spreekuur, omdat zij vorige maand verlies van "
    "reuk na covid infectie aangaf. Zij had geen last meer van kortademigheid, "
    "wel was er nog sprake van hoesten, geen afname vermoeidheid."
)


doc = nlp(text)

Find information in the doc object:

from spacy import displacy

displacy.render(doc, style='ent')

With relevant qualifiers:

for ent in doc.ents:
  print(ent, ent.start, ent.end, ent._.qualifiers)

• 11 14 verlies van reuk {'Temporality.HISTORICAL'}
• 25 26 kortademigheid {'Negation.NEGATED'}
• 33 34 hoesten {}
• 37 38 vermoeidheid {}

Documentation

Introduction

clinlp is built on top of spaCy, a widely used library for Natural Language Processing. Before getting started with clinlp, it may be useful to read spaCy 101: Everything you need to know (~10 mins). Main things to know are that spaCy consists of a tokenizer (breaks a text up into small pieces, i.e. words), and various components that further process the text.

Currently, clinlp offers the following components, tailored to Dutch Clinical text, further discussed below:

1. Tokenizer
2. Sentence splitter
3. Entity matcher (builtin Spacy)
4. Context detection

Tokenizer

The clinlp tokenizer is builin the blank model:

nlp = spacy.blank('clinlp')

It employs some custom rule based logic, including:

• Clinical text-specific logic for splitting punctuation, units, dosages (e.g. 20mg/dag :arrow_right: 20 mg / dag)
• Custom lists of abbreviations, units (e.g. pt., zn., mmHg)
• Custom tokenizing rules (e.g. xdd :arrow_right: x dd)
• Regarding DEDUCE tags as a single token (e.g. [DATUM-1]).
  • Deidentification is not builtin clinlp and should be done as a preprocessing step.

Sentence splitter

The sentence splitter can be added as follows:

nlp.add_pipe('clinlp_sentencizer')

It is designed to detect sentence boundaries in clinical text, whenever a character that demarks a sentence ending is matched (e.g. newline, period, question mark). It also correctly detects items in an enumerations (e.g. starting with - or *).

Entity matcher

Currently, the spaCy builtin PhraseMatcher and Matcher can be used for finding (named) entities in text. The first one accepts literal phrases only, that are matched in the tokenized text, while the second only also accepts spaCy patterns. These are not tailored for the clinical domain, but nevertheless useful when a somewhat coherent list of relevant patterns can be generated/obtained.

For instance, a matcher that helps recognize COVID19 symptoms:

ruler = nlp.add_pipe('entity_ruler')

terms = {
    'covid_19_symptomen': [
        'verkouden', 'neusverkouden', 'loopneus', 'niezen', 
        'keelpijn', 'hoesten', 'benauwd', 'kortademig', 'verhoging', 
        'koorts', 'verlies van reuk', 'verlies van smaak'
    ]
}

for term_description, terms in terms.items():
    ruler.add_patterns([{'label': term_description, 'pattern': term} for term in terms])

For more info, it's useful to check out these spaCy documentation pages:

• Rule based matching
• [spaCy API] Matcher
• [spaCy API] PhraseMatcher

Note that the DependencyMatcher cannot be used, and neither are part of speech tags available, as no good models for determining this information for clinical text exist (yet).

Context detection

After finding entities, it's often useful to qualify these entities, e.g.: are they negated or affirmed, historical or current? clinlp currently implements the rule-based Context algorithm for this purpose. This algorithm is fairly accurate, and quite transparent and fast. Better solutions will hopefully be added to clinlp in the future.

A set of rules, that checks for negation, temporality, plausibility and experiencer, is loaded by default:

nlp.add_pipe('clinlp_context_matcher')

A custom set of rules, including different types of qualifiers, can easily be defined. See clinlp/resources/psynlp_context_rules.json for an example, and load it as follows:

from clinlp.component.qualifier import parse_rules

cm = nlp.add_pipe('clinlp_context_matcher', config={'default_rules': None})
cm.add_rules(parse_rules('my_custom_rules.json'))

Where to go from here

We hope to extend clinlp with new functionality and more complete documentation in the near future. In the meantime, if any questions or problems arise, we recommend:

• Checking the source code
• Getting in touch (email | issue)

Principles and goals

Functional:

• Provides NLP pipelines optimized for Dutch clinical text
  • Performant and production-ready
  • Useful out-of-the-box, but highly configurable
• A single place to visit for your Dutch clinical NLP needs
• (Re-)uses existing components where possible, implements new components where needed
• Not intended for annotating, training, and analysis — already covered by existing packages

Development:

• Free and open source
• Targeted towards the technical user
• Curated and maintained by the Dutch Clinical NLP community
• Built using the spaCy framework (>3.0.0)
  • Therefore non-destructive
• Work towards some level of standardization of components (abstraction, protocols)
• Follows industry best practices (system design, code, documentation, testing, CI/CD)

Overarching goals:

• Improve the quality of Dutch Clinical NLP pipelines
• Enable easier (re)use/valorization of efforts
• Help mature the field of Dutch Clinical NLP
• Help develop the Dutch Clinical NLP community