Description
This model classifies if a text is ADE-related (True) or not (False).
Predicted Entities
True, False.
Live Demo Open in Colab Copy S3 URI
How to use
To classify your text if it is ADE-related, you can use this model as part of an nlp pipeline with the following stages: DocumentAssembler, SentenceDetector, Tokenizer, BertEmbeddings (biobert_pubmed_base_cased), SentenceEmbeddings, ClassifierDLModel.
...
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")
...
val embeddings = BertEmbeddings.pretrained('biobert_pubmed_base_cased')
.setInputCols(Array("document", 'token'))
.setOutputCol("word_embeddings")
val sentence_embeddings = SentenceEmbeddings()
.setInputCols(Array("document", "word_embeddings"))
.setOutputCol("sentence_embeddings")
.setPoolingStrategy("AVERAGE")
val classsifierADE = ClassifierDLModel.pretrained("classifierdl_ade_biobert", "en", "clinical/models")
.setInputCols(Array("sentence", "sentence_embeddings"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, embeddings, sentence_embeddings, classifierADE))
val data = Seq("I feel a bit drowsy & have a little blurred vision after taking an insulin").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
True : The sentence is talking about a possible ADE
False : The sentences doesn’t have any information about an ADE.
'True'
Model Information
| Model Name: | classifierdl_ade_biobert |
| Type: | ClassifierDLModel |
| Compatibility: | Healthcare NLP 2.6.2 + |
| Edition: | Official |
| License: | Licensed |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [class] |
| Language: | [en] |
| Case sensitive: | True |
Data Source
Trained on a custom dataset comprising of CADEC, DRUG-AE, Twimed using biobert_pubmed_base_cased embeddings.
Benchmarking
| | label | prec | rec | f1 |
|---:|-----------------:|-------:|-------:|-------:|
| 0 | False | 0.9469 | 0.9327 | 0.9398 |
| 1 | True | 0.7603 | 0.8030 | 0.7811 |
| 2 | Macro-average | 0.8536 | 0.8679 | 0.8604 |
| 3 | Weighted-average | 0.9077 | 0.9055 | 0.9065 |