# Qualifying entities

In the previous tutorial, we saw how to match a terminology on a text. Using the `#!python doc.ents` attribute, we can check whether a document mentions a concept of interest to build a cohort or

describe patients.

## The issue

However, consider the classical example where we look for the `diabetes` concept:

=== "French"

    ```

    Le patient n'est pas diabétique.

    Le patient est peut-être diabétique.

    Le père du patient est diabétique.

    ```

=== "English"

    ```

    The patient is not diabetic.

    The patient could be diabetic.

    The patient's father is diabetic.

    ```

None of these expressions should be used to build a cohort: the detected entity is either negated, speculative, or does not concern the patient themself. That's why we need to **qualify the matched

entities**.

!!! warning

    We show an English example just to explain the issue.

    EDS-NLP remains a **French-language** medical NLP library.

## The solution

We can use EDS-NLP's qualifier pipes to achieve that. Let's add specific components to our pipeline to detect these three modalities.

### Adding qualifiers

Adding qualifier pipes is straightforward:

```python hl_lines="25-29"

import edsnlp, edsnlp.pipes as eds

text = (

    "Motif de prise en charge : probable pneumopathie à COVID19, "

    "sans difficultés respiratoires\n"

    "Le père du patient est asthmatique."

)

regex = dict(

    covid=r"(coronavirus|covid[-\s]?19)",

    respiratoire=r"respiratoires?",

)

terms = dict(respiratoire="asthmatique")

nlp = edsnlp.blank("eds")

nlp.add_pipe(

    eds.matcher(

        regex=regex,

        terms=terms,

        attr="LOWER",

    ),

)

nlp.add_pipe(eds.sentences())  # (1)

nlp.add_pipe(eds.negation())  # Negation component

nlp.add_pipe(eds.hypothesis())  # Speculation pipe

nlp.add_pipe(eds.family())  # Family context detection

```

1. Qualifiers pipes need sentence boundaries to be set (see the [specific documentation](../pipes/qualifiers/index.md) for detail).

This code is complete, and should run as is.

### Reading the results

Let's output the results as a pandas DataFrame for better readability:

```python hl_lines="2 34-48"

import edsnlp, edsnlp.pipes as eds

import pandas as pd

text = (

    "Motif de prise en charge : probable pneumopathie à COVID19, "

    "sans difficultés respiratoires\n"

    "Le père du patient est asthmatique."

)

regex = dict(

    covid=r"(coronavirus|covid[-\s]?19)",

    respiratoire=r"respiratoires?",

)

terms = dict(respiratoire="asthmatique")

nlp = edsnlp.blank("eds")

nlp.add_pipe(

    eds.matcher(

        regex=regex,

        terms=terms,

        attr="LOWER",

    ),

)

nlp.add_pipe(eds.sentences())

nlp.add_pipe(eds.negation())  # Negation component

nlp.add_pipe(eds.hypothesis())  # Speculation pipe

nlp.add_pipe(eds.family())  # Family context detection

doc = nlp(text)

# Extraction as a pandas DataFrame

entities = []

for ent in doc.ents:

    d = dict(

        lexical_variant=ent.text,

        label=ent.label_,

        negation=ent._.negation,

        hypothesis=ent._.hypothesis,

        family=ent._.family,

    )

    entities.append(d)

df = pd.DataFrame.from_records(entities)

```

This code is complete, and should run as is.

We get the following result:

| lexical_variant | label        | negation | hypothesis | family |

|:----------------|:-------------|----------|------------|--------|

| COVID19         | covid        | False    | True       | False  |

| respiratoires   | respiratoire | True     | False      | False  |

| asthmatique     | respiratoire | False    | False      | True   |

## Conclusion

The qualifier pipes limits the number of false positives by detecting linguistic modulations such as negations or speculations.

Go to the [full documentation](/pipes/qualifiers) for a complete presentation of the different pipes,

their configuration options and validation performance.