Suicide Attempt

The eds.suicide_attempt pipeline component detects mentions of Suicide Attempt. It can be used with a span qualifier for contextualisation of the entity (history) and to detect false positives as negation, hypothesis or family. We recommend to use a machine learning qualifier to disambiguate polysemic words, as proposed in Bey et al., 2024.

Every matched entity will be labelled suicide_attempt.

Extensions

Each entity span will have the suicide attempt modality as an attribute. The available modalities are:

suicide_attempt_unspecific
autolysis
intentional_drug_overdose
jumping_from_height
cuts
strangling
self_destructive_behavior
burn_gas_caustic

Examples

import edsnlp, edsnlp.pipes as eds

nlp = edsnlp.blank("eds")
nlp.add_pipe(eds.suicide_attempt())

text = "J'ai vu le patient à cause d'une IMV avec paracetamol"
doc = nlp(text)
doc.ents
# Out: (IMV,)

ent = doc.ents[0]
ent._.suicide_attempt_modality
# Out: 'intentional_drug_overdose'

Patterns used for the named entity recognition

# fmt: off
patterns = {
    "suicide_attempt_unspecific": [
        r"\b(?<!\.)(?<!Voie\s\d\s\:\s)(?<!Voie\sd.abord\s\:\s)(?<!surface\s)(?<!d[ée]sorientation\s)(?<!abord\s)(?<!ECG\s:\s)(?<!volume\s)(?<!\d\s[mc]m\sde\sla\s)(?<!\d[mc]m\sde\sla\s)(?<!au\scontact\sde\sla\s)T\.?S\.?(?![\.A-Za-z])(?!\sapyr[eé]tique)(?!.+TRANSSEPTAL)(?!.+T[34])(?!.+en\sr.gression)\b",
        r"(?<!\.)T\.S\.(?![A-Za-z])",
        r"\b(?<!.)TS\.\B",
        r"(?i)tentative[s]?\s+de\s+sui?cide",
        r"(?i)tent[ée]\s+de\s+((se\s+(suicider|tuer))|(mettre\s+fin\s+[àa]\s+((ses\s+jours?)|(sa\s+vie))))",
    ],
    "autolysis": [r"(?i)tentative\s+d'autolyse", r"(?i)autolyse"],
    "intentional_drug_overdose": [
        r"(?i)(intoxication|ingestion)\s+m[ée]dicamenteuse\s+volontaire",
        r"(?i)\b(i\.?m\.?v\.?)\b",
        r"(?i)(intoxication|ingestion)\s*([a-zA-Z0-9_éàèôê\-]+\s*){0,3}\s*volontaire",
        r"TS\s+med\s+polymedicamenteuse",
        r"TS\s+(poly)?([\s-])?m[ée]dicamenteuse",
    ],
    "jumping_from_height": [
        r"(?i)tentative[s]?\s+de\s+d[ée]fenestration",
        r"(?i)(?<!id[ée]es?\sde\s)d[ée]fenestration(?!\saccidentelle)",
        r"(?i)d[ée]fenestration\s+volontaire",
        r"(?i)d[ée]fenestration\s+intentionnelle",
        r"(?i)jet.r?\sd.un\spont",
    ],
    "cuts": [r"(?i)phl[ée]botomie"],
    "strangling": [r"(?i)pendaison"],
    "self_destructive_behavior": [r"(?i)autodestruction"],
    "burn_gas_caustic": [r"(?i)ing[eé]stion\sde\s(produit\s)?caustique"],
}
# fmt: on

Parameters

PARAMETER	DESCRIPTION
`nlp`	The pipeline object. TYPE: `PipelineProtocol`
`name`	The name of the pipe TYPE: `str`
`attr`	Attribute to match on, eg `TEXT`, `NORM`, etc. TYPE: `Union[str, Dict[str, str]]`
`ignore_excluded`	Whether to skip excluded tokens during matching. TYPE: `bool`
`ignore_space_tokens`	Whether to skip space tokens during matching. TYPE: `bool`
`patterns`	The regex pattern to use TYPE: `List[str]`
`span_setter`	How to set matches on the doc TYPE: `SpanSetterArg`
`label`	Label name to use for the `Span` object and the extension TYPE: `str`

RETURNS	DESCRIPTION
`SuicideAttemptMatcher`

Authors and citation

The eds.suicide_attempt component was developed by AP-HP's Data Science team, following the insights of the algorithm proposed by Bey et al., 2024.

Bey R., Cohen A., Trebossen V., Dura B., Geoffroy P., Jean C., Landman B., Petit-Jean T., Chatellier G., Sallah K., Tannier X., Bourmaud A. and Delorme R., 2024. Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality. 10.1038/s44184-023-00046-7