CKD

The eds.CKD pipeline component extracts mentions of CKD (Chronic Kidney Disease). It will notably match:

Mentions of various diseases (see below)
Kidney transplantation
Chronic dialysis
Renal failure from stage 3 to 5. The stage is extracted by trying 3 methods:
- Extracting the mentioned stage directly ("IRC stade IV")
- Extracting the severity directly ("IRC terminale")
- Extracting the mentioned GFR (DFG in french) ("IRC avec DFG estimé à 30 mL/min/1,73m2)")

Details of the used patterns

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Extensions

On each span span that match, the following attributes are available:

span._.detailed_status: set to None
span._.assigned: dictionary with the following keys, if relevant:
- stage: mentioned renal failure stage
- status: mentioned renal failure severity (e.g. modérée, sévère, terminale, etc.)
- dfg: mentioned DFG

Examples

import edsnlp, edsnlp.pipes as eds

nlp = edsnlp.blank("eds")
nlp.add_pipe(eds.sentences())
nlp.add_pipe(
    eds.normalizer(
        accents=True,
        lowercase=True,
        quotes=True,
        spaces=True,
        pollution=dict(
            information=True,
            bars=True,
            biology=True,
            doctors=True,
            web=True,
            coding=True,
            footer=True,
        ),
    ),
)
nlp.add_pipe(eds.ckd())

Below are a few examples:

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text = "Patient atteint d'une glomérulopathie."
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: [glomérulopathie]

text = "Patient atteint d'une tubulopathie aigüe."
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: []

text = "Patient transplanté rénal"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: [transplanté rénal]

text = "Présence d'une insuffisance rénale aigüe sur chronique"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: [insuffisance rénale aigüe sur chronique]

text = "Le patient a été dialysé"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: []

text = "Le patient est dialysé chaque lundi"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: [dialysé chaque lundi]

span = spans[0]

span._.assigned
# Out: {'chronic': [lundi]}

text = "Présence d'une IRC"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: []

text = "Présence d'une IRC sévère"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: [IRC sévère]

span = spans[0]

span._.assigned
# Out: {'status': sévère}

text = "Présence d'une IRC au stade IV"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: [IRC au stade IV]

span = spans[0]

span._.assigned
# Out: {'stage': IV}

text = "Présence d'une IRC avec DFG à 30"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: [IRC avec DFG à 30]

span = spans[0]

span._.assigned
# Out: {'dfg': 30}

text = "Présence d'une maladie rénale avec DFG à 110"
doc = nlp(text)
spans = doc.spans["ckd"]

spans
# Out: []

Parameters

PARAMETER	DESCRIPTION
`nlp`	The pipeline TYPE: `Optional[PipelineProtocol]`
`name`	The name of the component TYPE: `Optional[str]`
`patterns`	The patterns to use for matching DEFAULT: `[{'source': 'main', 'regex': ['glomerulonephrit...`
`label`	The label to use for the `Span` object and the extension TYPE: `str` DEFAULT: `ckd`
`span_setter`	How to set matches on the doc TYPE: `SpanSetterArg` DEFAULT: `{'ents': True, 'ckd': True}`

Authors and citation

The eds.ckd component was developed by AP-HP's Data Science team with a team of medical experts, following the insights of the algorithm proposed by Petit-Jean et al., 2024.

Petit-Jean T., Gérardin C., Berthelot E., Chatellier G., Frank M., Tannier X., Kempf E. and Bey R., 2024. Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions. Journal of the American Medical Informatics Association. 31, pp.1280-1290. 10.1093/jamia/ocae069