Tutorials

We provide step-by-step guides to get you started. We cover the following use-cases:

• Matching a terminology: you're looking for a concept within a corpus of texts.
• Qualifying entities: you want to make sure that the concept you've extracted are not invalidated by linguistic modulation.
• Detecting dates, which could serve as the basis for an event ordering algorithm.
• Processing multiple texts: to improve the inference speed of your pipeline !
• Detecting Hospitalisation Reason: you want to look spans that mention the reason of hospitalisation or tag entities as the reason.
• Detecting false endlines: classify each endline and add the attribute excluded to the these tokens.

Rationale

In a typical medical NLP pipeline, a group of clinicians would define a list of synonyms for a given concept of interest (say, for example, diabetes), and look for that terminology in a corpus of documents.

Now, consider the following example:

FrenchEnglish

Le patient n'est pas diabétique.
Le patient est peut-être diabétique.
Le père du patient est diabétique.

The patient is not diabetic.
The patient could be diabetic.
The patient's father is diabetic.

There is an obvious problem: none of these examples should lead us to include this particular patient into the cohort.

Warning

We show an English example just to explain the issue. EDS-NLP remains a French-language medical NLP library.

To curb this issue, EDS-NLP proposes rule-based pipelines that qualify entities to help the user make an informed decision about which patient should be included in a real-world data cohort.

To sum up, a typical medical NLP project consists in:

1. Editing a terminology
2. "Matching" this terminology on a corpus, ie extract phrases that belong to that terminology
3. "Qualifying" entities to avoid false positives

Once the pipeline is ready, we need to deploy it efficiently.