Description
Named Entity recognition annotator allows for a generic model to be trained by utilizing a deep learning algorithm (Char CNNs - BiLSTM - CRF - word embeddings) inspired on a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM,CNN.
This model has been trained to extract genes/proteins from a medical text for PySpark 2.4.x users.
Predicted Entities
GENE_PROTEIN
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetectorDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical" ,"en", "clinical/models")\
.setInputCols(["sentence","token"])\
.setOutputCol("embeddings")
ner = MedicalNerModel.pretrained("ner_biomedical_bc2gm", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[
document_assembler,
sentenceDetectorDL,
tokenizer,
word_embeddings,
ner,
ner_converter])
data = spark.createDataFrame([["Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections."]]).toDF("text")
result = nlpPipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetectorDL =
SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical" ,"en", "clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("word_embeddings")
val ner = MedicalNerModel.pretrained("ner_biomedical_bc2gm", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "word_embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sentenceDetectorDL,
tokenizer,
word_embeddings,
ner,
ner_converter))
val data = Seq("Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.biomedical_bc2gm").predict("""Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.""")
Results
+-----------+------------+
|chunk |ner_label |
+-----------+------------+
|S-100 |GENE_PROTEIN|
|HMB-45 |GENE_PROTEIN|
|cytokeratin|GENE_PROTEIN|
+-----------+------------+
Model Information
| Model Name: | ner_biomedical_bc2gm |
| Compatibility: | Healthcare NLP 3.5.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.6 MB |
References
Created by Smith et al. at 2008, the BioCreative II Gene Mention Recognition (BC2GM) Dataset contains data where participants are asked to identify a gene mention in a sentence by giving its start and end characters. The training set consists of a set of sentences, and for each sentence a set of gene mentions (GENE annotations).
Benchmarking
label precision recall f1-score support
GENE_PROTEIN 0.83 0.82 0.82 6325
micro-avg 0.83 0.82 0.82 6325
macro-avg 0.83 0.82 0.82 6325
weighted-avg 0.83 0.82 0.82 6325