Detect Drug Chemicals (BertForTokenClassifier)

Description

Pretrained named entity recognition deep learning model for Drugs. This model is traiend with BertForTokenClassification method from transformers library and imported into Spark NLP. It detects drug chemicals.

Predicted Entities

DrugChem

Live Demo Open in Colab Copy S3 URI

How to use

documentAssembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentenceDetector = SentenceDetector() \
    .setInputCols(["document"]) \
    .setOutputCol("sentence")

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

tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_ner_drugs", "en", "clinical/models")\
    .setInputCols("token", "sentence")\
    .setOutputCol("ner")\
    .setCaseSensitive(True)

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

pipeline =  Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier, ner_converter])

model = pipeline.fit(spark.createDataFrame(pd.DataFrame({'text': ['']})))

test_sentence = """The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes."""

result = model.transform(spark.createDataFrame(pd.DataFrame({'text': [test_sentence]})))
val documentAssembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val sentenceDetector = new SentenceDetector()
    .setInputCols("document")
    .setOutputCol("sentence")

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

val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_ner_drugs", "en", "clinical/models")
    .setInputCols(Array("token", "sentence"))
    .setOutputCol("ner")
    .setCaseSensitive(True)

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

val pipeline =  new Pipeline().setStages(Array(documentAssembler, sentenceDetector, tokenizer, tokenClassifier, ner_converter))

val data = Seq("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.""").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.token_bert.ner_drugs").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.""")

Results

+--------------+---------+
|chunk         |ner_label|
+--------------+---------+
|potassium     |DrugChem |
|nucleotide    |DrugChem |
|anthracyclines|DrugChem |
|taxanes       |DrugChem |
|vinorelbine   |DrugChem |
|vinorelbine   |DrugChem |
|anthracyclines|DrugChem |
|taxanes       |DrugChem |
+--------------+---------+

Model Information

Model Name: bert_token_classifier_ner_drugs
Compatibility: Healthcare NLP 3.2.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token]
Output Labels: [ner]
Language: en
Case sensitive: true
Max sentense length: 128

Data Source

Trained on i2b2_med7 + FDA. https://www.i2b2.org/NLP/Medication

Benchmarking

label          precision    recall  f1-score   support
B-DrugChem       0.99        0.99      0.99     97872
I-DrugChem       0.99        0.99      0.99     54909
O                1.00        1.00      1.00   1191109
accuracy          -           -        1.00   1343890
macro-avg        0.99        0.99      0.99   1343890
weighted-avg     1.00        1.00      1.00   1343890