Healthsea

Healthsea: an end-to-end spaCy pipeline for exploring health supplement effects

This spaCy project trains an NER model and a custom Text Classification model with Clause Segmentation and Blinding capabilities to analyze supplement reviews and their potential effects on health.

Example
import spacy

nlp = spacy.load("en_healthsea")
doc = nlp("This is great for joint pain.")

# Clause Segmentation & Blinding
print(doc._.clauses)

>     {
>    "split_indices": [0, 7],
>    "has_ent": true,
>    "ent_indices": [4, 6],
>    "blinder": "_CONDITION_",
>    "ent_name": "joint pain",
>    "cats": {
>        "POSITIVE": 0.9824668169021606,
>        "NEUTRAL": 0.017364952713251114,
>        "NEGATIVE": 0.00002889777533710003,
>        "ANAMNESIS": 0.0001394189748680219
>    "prediction_text": ["This", "is", "great", "for", "_CONDITION_", "!"]
>    }

# Aggregated results
>    {
>    "joint_pain": {
>        "effects": ["POSITIVE"],
>        "effect": "POSITIVE",
>        "label": "CONDITION",
>        "text": "joint pain"
>       }
>    }

Author info

Edward Schmuhl

GitHubexplosion/healthsea

Categories pipeline research

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