Detect normalized genes and human phenotypes

Description

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

Predicted Entities:

GO, HP

Live Demo Open in ColabDownload

How to use

Use as part of an nlp pipeline with the following stages: DocumentAssembler, SentenceDetector, Tokenizer, WordEmbeddingsModel, NerDLModel. Add the NerConverter to the end of the pipeline to convert entity tokens into full entity chunks.


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

nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter])

light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))

annotations = light_pipeline.fullAnnotate("Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.")

Results

+----+--------------------------+---------+-------+----------+
|    | chunk                    |   begin |   end | entity   |
+====+==========================+=========+=======+==========+
|  0 | tumor                    |      39 |    43 | HP       |
+----+--------------------------+---------+-------+----------+
|  1 | tricarboxylic acid cycle |      79 |   102 | GO       |
+----+--------------------------+---------+-------+----------+

Model Information

Model Name: ner_human_phenotype_go_clinical
Type: ner
Compatibility: Spark NLP for Healthcare 2.6.0 +
Edition: Official
License: Licensed
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: [en]
Case sensitive: false