Description
Classify text/sentence in two categories:
-
True: The sentence is talking about a possible ADE -
False: The sentences doesn’t have any information about an ADE.
Predicted Entities
True, False
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained('biobert_pubmed_base_cased')\
.setInputCols(["document", 'token'])\
.setOutputCol("word_embeddings")
sentence_embeddings = SentenceEmbeddings() \
.setInputCols(["document", "word_embeddings"]) \
.setOutputCol("sentence_embeddings") \
.setPoolingStrategy("AVERAGE")
classifier = ClassifierDLModel.pretrained('classifierdl_ade_biobert', 'en', 'clinical/models')\
.setInputCols(['document', 'token', 'sentence_embeddings'])\
.setOutputCol('class')
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, embeddings, sentence_embeddings, classifier])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
annotations = light_pipeline.fullAnnotate(["I feel a bit drowsy & have a little blurred vision after taking an insulin", "I feel great after taking tylenol"])
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val embeddings = BertEmbeddings.pretrained("biobert_pubmed_base_cased")
.setInputCols(Array("document", "token"))
.setOutputCol("word_embeddings")
val sentence_embeddings = new SentenceEmbeddings()
.setInputCols(Array("document", "word_embeddings"))
.setOutputCol("sentence_embeddings")
.setPoolingStrategy("AVERAGE")
val classifier = ClassifierDLModel.pretrained("classifierdl_ade_biobert", "en", "clinical/models")
.setInputCols(Array("document", "token", "sentence_embeddings"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, embeddings, sentence_embeddings, classifier))
val data = Seq("""I feel a bit drowsy & have a little blurred vision after taking an insulin, I feel great after taking tylenol""").toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.ade.biobert").predict("""I feel a bit drowsy & have a little blurred vision after taking an insulin""")
Results
| | text | label |
|--:|:---------------------------------------------------------------------------|:------|
| 0 | I feel a bit drowsy & have a little blurred vision after taking an insulin | True |
| 1 | I feel great after taking tylenol | False |
Model Information
| Model Name: | classifierdl_ade_biobert |
| Compatibility: | Healthcare NLP 2.7.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [class] |
| Language: | en |
| Dependencies: | biobert_pubmed_base_cased |
Data Source
Trained on a custom dataset comprising of CADEC, DRUG-AE and Twimed.
Benchmarking
label precision recall f1-score support
False 0.96 0.94 0.95 6923
True 0.71 0.79 0.75 1359
micro-avg 0.91 0.91 0.91 8282
macro-avg 0.83 0.86 0.85 8282
weighted-avg 0.92 0.91 0.91 8282