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 Download
How to use
...
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")
...
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_human_phenotype_go_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[String]).transform(data)
Model Information
| Model Name: | ner_human_phenotype_go_biobert |
| Compatibility: | Spark NLP for Healthcare 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