Detect Posology concepts (ner_posology_healthcare)

Description

Detect Drug, Dosage and administration instructions in text using pretraiend NER model.

Predicted Entities

Drug, Duration, Strength, Form, Frequency, Dosage, Route

Live Demo Open in Colab Download

How to use


...
embeddings_clinical = WordEmbeddingsModel.pretrained("embeddings_healthcare", "en", "clinical/models")  .setInputCols(["sentence", "token"])  .setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_posology_healthcare", "en", "clinical/models")   .setInputCols(["sentence", "token", "embeddings"])   .setOutputCol("ner")
...
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings_clinical, clinical_ner, ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["EXAMPLE_TEXT"]]).toDF("text"))

...
val embeddings_clinical = WordEmbeddingsModel.pretrained("embeddings_healthcare", "en", "clinical/models")
  .setInputCols(Array("sentence", "token"))
  .setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_posology_healthcare", "en", "clinical/models")
  .setInputCols(Array("sentence", "token", "embeddings"))
  .setOutputCol("ner")
...
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings_clinical, ner, ner_converter))
val result = pipeline.fit(Seq.empty[""].toDS.toDF("text")).transform(data)

Model Information

Model Name: ner_posology_healthcare
Compatibility: Spark NLP for Healthcare 3.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en