Description
Pretrained named entity recognition deep learning model for clinical terms. The SparkNLP deep learning model (MedicalNerModel) is inspired by a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM-CNN.
Predicted Entities
PROBLEM, TEST, TREATMENT.
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = 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")
clinical_ner = MedicalNerModel.pretrained("ner_clinical", "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,
word_embeddings,
clinical_ner,
ner_converter])
data = spark.createDataFrame([["""Mr. ABC is a 60-year-old gentleman who had stress test earlier today in my office with severe chest pain after 5 minutes of exercise on the standard Bruce with horizontal ST depressions and moderate apical ischemia on stress imaging only. He required 3 sublingual nitroglycerin in total. The patient underwent cardiac catheterization with myself today which showed mild-to-moderate left main distal disease of 30%, a severe mid-LAD lesion of 99%, and a mid-left circumflex lesion of 80% with normal LV function and some mild luminal irregularities in the right coronary artery with some moderate stenosis seen in the mid to distal right PDA."""]]).toDF("text")
result = nlpPipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new 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("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_clinical", "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,
word_embeddings,
ner,
ner_converter))
val data = Seq("""Mr. ABC is a 60-year-old gentleman who had stress test earlier today in my office with severe chest pain after 5 minutes of exercise on the standard Bruce with horizontal ST depressions and moderate apical ischemia on stress imaging only. He required 3 sublingual nitroglycerin in total. The patient underwent cardiac catheterization with myself today which showed mild-to-moderate left main distal disease of 30%, a severe mid-LAD lesion of 99%, and a mid-left circumflex lesion of 80% with normal LV function and some mild luminal irregularities in the right coronary artery with some moderate stenosis seen in the mid to distal right PDA.""").toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.clinical").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.""")
Results
|chunk |begin|end|ner_label|
+-------------------------------------------------------------+-----+---+---------+
|stress test |43 |53 |TEST |
|severe chest pain |87 |103|PROBLEM |
|horizontal ST depressions |160 |184|PROBLEM |
|moderate apical ischemia |190 |213|PROBLEM |
|stress imaging |218 |231|TEST |
|3 sublingual nitroglycerin |251 |276|TREATMENT|
|cardiac catheterization |310 |332|TEST |
|mild-to-moderate left main distal disease of 30% |365 |412|PROBLEM |
|a severe mid-LAD lesion |415 |437|PROBLEM |
|a mid-left circumflex lesion |451 |478|PROBLEM |
|some mild luminal irregularities in the right coronary artery|515 |575|PROBLEM |
|some moderate stenosis |582 |603|PROBLEM |
+-------------------------------------------------------------+-----+---+---------+
Model Information
| Model Name: | ner_clinical |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
Trained on augmented version of 2010 i2b2 challenge data with ‘embeddings_clinical’. https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
Benchmarking
| | label | tp | fp | fn | prec | rec | f1 |
|---:|--------------:|------:|------:|------:|---------:|---------:|---------:|
| 0 | I-TREATMENT | 6625 | 1187 | 1329 | 0.848054 | 0.832914 | 0.840416 |
| 1 | I-PROBLEM | 15142 | 1976 | 2542 | 0.884566 | 0.856254 | 0.87018 |
| 2 | B-PROBLEM | 11005 | 1065 | 1587 | 0.911765 | 0.873968 | 0.892466 |
| 3 | I-TEST | 6748 | 923 | 1264 | 0.879677 | 0.842237 | 0.86055 |
| 4 | B-TEST | 8196 | 942 | 1029 | 0.896914 | 0.888455 | 0.892665 |
| 5 | B-TREATMENT | 8271 | 1265 | 1073 | 0.867345 | 0.885167 | 0.876165 |
| 6 | Macro-average | 55987 | 7358 | 8824 | 0.881387 | 0.863166 | 0.872181 |
| 7 | Micro-average | 55987 | 7358 | 8824 | 0.883842 | 0.86385 | 0.873732 |