Description
Detect adverse reactions of drugs in reviews, tweets, and medical text using pretrained NER model.
Predicted Entities
DRUG, ADE
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 = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_ade_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,
embeddings_clinical,
clinical_ner,
ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = model.transform(spark.createDataFrame([["Hypersensitivity to aspirin can be manifested as acute asthma, urticaria and/or angioedema, or a systemic anaphylactoid reaction."]]).toDF("text"))
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val embeddings_clinical = WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")
.setInputCols(Array("sentence","token"))
.setOutputCol("embeddings")
val clinical_ner = MedicalNerModel.pretrained("ner_ade_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 nlpPipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
embeddings_clinical,
clinical_ner,
ner_converter))
val model = nlpPipeline.fit(Seq("").toDF("text"))
val result = model.transform(Seq("Hypersensitivity to aspirin can be manifested as acute asthma, urticaria and/or angioedema, or a systemic anaphylactoid reaction.").toDF("text"))
import nlu
nlu.load("en.med_ner.ade.clinical").predict("""Hypersensitivity to aspirin can be manifested as acute asthma, urticaria and/or angioedema, or a systemic anaphylactoid reaction.""")
Result
+-------------------------------+-----+---+---------+
|chunk |begin|end|ner_label|
+-------------------------------+-----+---+---------+
|aspirin |20 |26 |DRUG |
|acute asthma |49 |60 |ADE |
|urticaria |63 |71 |ADE |
|angioedema |80 |89 |ADE |
|systemic anaphylactoid reaction|97 |127|ADE |
+-------------------------------+-----+---+---------+
Model Information
| Model Name: | ner_ade_clinical |
| 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|
+------+-------+------+------+-------+---------+------+------+
| DRUG|17470.0|1436.0|1951.0|19421.0| 0.924|0.8995|0.9116|
| ADE| 6010.0|1244.0|1886.0| 7896.0| 0.8285|0.7611|0.7934|
+------+-------+------+------+-------+---------+------+------+
+------------------+
| macro|
+------------------+
|0.8525141088742945|
+------------------+
+------------------+
| micro|
+------------------+
|0.8774545383517981|
+------------------+