Detect Normalized Genes and Human Phenotypes (biobert)

Description

This model can be used to detect normalized mentions of genes (go) and human phenotypes (hp) in medical text.

Predicted Entities

HP, GO

Live Demo Open in Colab Copy S3 URI

How to use

document_assembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")
         
sentence_detector = SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

embeddings_clinical = BertEmbeddings.pretrained("biobert_pubmed_base_cased")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("embeddings")

clinical_ner = MedicalNerModel.pretrained("ner_human_phenotype_go_biobert", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("ner")

ner_converter = NerConverter()\
 	.setInputCols(["sentence", "token", "ner"])\
 	.setOutputCol("ner_chunk")

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 document_assembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")
         
val sentence_detector = new SentenceDetector()
    .setInputCols("document")
    .setOutputCol("sentence")

val tokenizer = new Tokenizer()
    .setInputCols("sentence")
    .setOutputCol("token")

val embeddings_clinical = BertEmbeddings.pretrained("biobert_pubmed_base_cased")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")

val ner = MedicalNerModel.pretrained("ner_human_phenotype_go_biobert", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("ner")

val ner_converter = new NerConverter()
 	.setInputCols(Array("sentence", "token", "ner"))
 	.setOutputCol("ner_chunk")

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings_clinical, ner, ner_converter))

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.human_phenotype.go_biobert").predict("""Put your text here.""")

Model Information

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

Benchmarking

entity      tp     fp     fn     total   precision  recall      f1
GO        7637.0  579.0  441.0  8078.0     0.9295   0.9454  0.9374
HP        1463.0  273.0  222.0  1685.0     0.8427   0.8682  0.8553
macro        -      -      -       -         -       -      0.89635
micro        -      -      -       -         -       -      0.92323