Description
This model is trained with the DocumentMLClassifierApproach annotator and classifies a text/sentence into two categories:
True : The sentence is talking about a possible ADE
False : The sentence doesn’t have any information about an ADE.
The corpus used for model training is ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data. This is a dataset for classification of a sentence if it is ADE-related (True) or not (False).
Predicted Entities
True, False
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols("document")\
.setOutputCol("token")
classifier_ml = DocumentMLClassifierModel.pretrained("classifierml_ade", "en", "clinical/models")\
.setInputCols("token")\
.setOutputCol("prediction")
clf_Pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
classifier_ml])
data = spark.createDataFrame([["""I feel great after taking tylenol."""], ["""Toxic epidermal necrolysis resulted after 19 days of treatment with 5-fluorocytosine and amphotericin B."""]]).toDF("text")
result = clf_Pipeline.fit(data).transform(data)
val document_assembler =new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val classifier_ml = new DocumentMLClassifierModel.pretrained("classifierml_ade", "en", "clinical/models")
.setInputCols("token")
.setOutputCol("prediction")
val clf_Pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, classifier_ml))
val data = Seq(Array("I feel great after taking tylenol", "Toxic epidermal necrolysis resulted after 19 days of treatment with 5-fluorocytosine and amphotericin B.")).toDS().toDF("text")
val result = clf_Pipeline.fit(data).transform(data)
Results
+--------------------------------------------------------------------------------------------------------+-------+
|text |result |
+--------------------------------------------------------------------------------------------------------+-------+
|Toxic epidermal necrolysis resulted after 19 days of treatment with 5-fluorocytosine and amphotericin B.|[True] |
|I feel great after taking tylenol |[False]|
+--------------------------------------------------------------------------------------------------------+-------+
Model Information
| Model Name: | classifierml_ade |
| Compatibility: | Healthcare NLP 5.3.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [token] |
| Output Labels: | [prediction] |
| Language: | en |
| Size: | 2.7 MB |
References
The corpus used for model training is ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data. This is a dataset for classification of a sentence if it is ADE-related (True) or not (False).
Reference: Gurulingappa et al., Benchmark Corpus to Support Information Extraction for Adverse Drug Effects, JBI, 2012. https://www.sciencedirect.com/science/article/pii/S1532046412000615
Benchmarking
label precision recall f1-score support
False 0.90 0.94 0.92 3359
True 0.85 0.75 0.79 1364
accuracy - - 0.89 4723
macro avg 0.87 0.85 0.86 4723
weighted avg 0.89 0.89 0.89 4723