Detect problem, test, treatment in medical text (biobert)

Description

Detect problem, test, treatment in medical text using pretrained NER model.

Predicted Entities

PROBLEM, TREATMENT, TEST

Live Demo Open in Colab Download

How to use


...
embeddings_clinical = BertEmbeddings.pretrained("biobert_pubmed_base_cased")  .setInputCols(["sentence", "token"])  .setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_clinical_biobert", "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 = BertEmbeddings.pretrained("biobert_pubmed_base_cased")
  .setInputCols(Array("sentence", "token"))
  .setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_clinical_biobert", "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_clinical_biobert
Compatibility: Spark NLP for Healthcare 3.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en