Description
Detect anatomical sites and references in medical text using pretrained NER model.
Predicted Entities
tissue_structure, Organism_substance, Developing_anatomical_structure, Cell, Cellular_component, Immaterial_anatomical_entity, Organ, Pathological_formation, Organism_subdivision, Anatomical_system, Tissue
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_anatomy_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_anatomy_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.anatomy.biobert").predict("""Put your text here.""")
Model Information
| Model Name: | ner_anatomy_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|
+-------------------------------+-----+----+----+-----+---------+------+------+
| Organ| 53.0|17.0|12.0| 65.0| 0.7571|0.8154|0.7852|
| Pathological_formation| 83.0|23.0|14.0| 97.0| 0.783|0.8557|0.8177|
| Organism_substance| 42.0| 1.0|14.0| 56.0| 0.9767| 0.75|0.8485|
| tissue_structure|131.0|28.0|49.0|180.0| 0.8239|0.7278|0.7729|
| Cellular_component| 17.0| 0.0|20.0| 37.0| 1.0|0.4595|0.6296|
| Tissue| 27.0| 4.0|16.0| 43.0| 0.871|0.6279|0.7297|
| Anatomical_system| 15.0| 3.0| 8.0| 23.0| 0.8333|0.6522|0.7317|
|Developing_anatomical_structure| 2.0| 1.0| 3.0| 5.0| 0.6667| 0.4| 0.5|
| Immaterial_anatomical_entity| 7.0| 2.0| 6.0| 13.0| 0.7778|0.5385|0.6364|
| Cell|180.0| 6.0|15.0|195.0| 0.9677|0.9231|0.9449|
| Organism_subdivision| 11.0| 5.0|10.0| 21.0| 0.6875|0.5238|0.5946|
+-------------------------------+-----+----+----+-----+---------+------+------+
+------------------+
| macro|
+------------------+
|0.7264701979913192|
+------------------+
+------------------+
| micro|
+------------------+
|0.8108878300337679|
+------------------+