Description
This is a pre-trained model that can be used to automatically detect all chemical compounds and gene mentions from medical texts.
Predicted Entities
: CHEMICAL, GENE-Y, GENE-N
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")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_chemprot_clinical", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."]]).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 word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_chemprot_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("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""").toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.chemprot.clinical").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
Results
+----+---------------------------------+---------+-------+----------+
| | chunk | begin | end | entity |
+====+=================================+=========+=======+==========+
| 0 | Keratinocyte growth factor | 0 | 25 | GENE-Y |
+----+---------------------------------+---------+-------+----------+
| 1 | acidic fibroblast growth factor | 31 | 61 | GENE-Y |
+----+---------------------------------+---------+-------+----------+
Model Information
| Model Name: | ner_chemprot_clinical |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
This model was trained on the ChemProt corpus using ‘embeddings_clinical’ embeddings. Make sure you use the same embeddings when running the model.
Benchmarking
| | label | tp | fp | fn | prec | rec | f1 |
|---:|:--------------|-------:|------:|-----:|---------:|---------:|---------:|
| 0 | B-GENE-Y | 4650 | 1090 | 838 | 0.810105 | 0.847303 | 0.828286 |
| 1 | B-GENE-N | 1732 | 981 | 1019 | 0.638408 | 0.629589 | 0.633968 |
| 2 | I-GENE-Y | 1846 | 571 | 573 | 0.763757 | 0.763125 | 0.763441 |
| 3 | B-CHEMICAL | 7512 | 804 | 1136 | 0.903319 | 0.86864 | 0.88564 |
| 4 | I-CHEMICAL | 1059 | 169 | 253 | 0.862378 | 0.807165 | 0.833858 |
| 5 | I-GENE-N | 1393 | 853 | 598 | 0.620214 | 0.699648 | 0.657541 |
| 6 | Macro-average | 18192 | 4468 | 4417 | 0.766363 | 0.769245 | 0.767801 |
| 7 | Micro-average | 18192 | 4468 | 4417 | 0.802824 | 0.804635 | 0.803729 |