Description
Detect symptoms, modifiers, age, drugs, treatments, tests and a lot more using a single pretrained NER model.
Predicted Entities
Symptom_Name
, Negated
, Pulse_Rate
, Negation
, Date_of_death
, Age
, Modifier
, Substance_Name
, Causative_Agents_(Virus_and_Bacteria)
, Drug_incident_description
, Diagnosis
, Weight
, Drug_Name
, Procedure_Name
, Lab_Name
, Blood_Pressure
, Cause_of_death
, Lab_Result
, Gender
, Name
, Temperature
, Procedure_Findings
, Section_Name
, Route
, Maybe
, O2_Saturation
, Respiratory_Rate
, Procedure
, Procedure_incident_description
, Frequency
, Dosage
, Allergenic_substance
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_jsl_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_jsl_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_jsl_biobert |
Compatibility: | Spark NLP for Healthcare 3.0.0+ |
License: | Licensed |
Edition: | Official |
Input Labels: | [sentence, token, embeddings] |
Output Labels: | [ner] |
Language: | en |