Description
Pretrained named entity recognition (NER) deep learning model for extracting clinical conditions from Spanish texts. The SparkNLP deep learning model (MedicalNerModel) is inspired by a former state of the art model for NER: Chiu & Nichols, Named Entity Recognition with Bidirectional LSTM-CNN.
The corpus used for model training is provided by European Clinical Case Corpus (E3C), a project aimed at offering a freely available multilingual corpus of semantically annotated clinical narratives.
Predicted Entities
clinical_condition
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetectorDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
word_embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d","es")\
.setInputCols(["sentence","token"])\
.setOutputCol("embeddings")
ner = MedicalNerModel.pretrained('ner_eu_clinical_condition', "es", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[
document_assembler,
sentenceDetectorDL,
tokenizer,
word_embeddings,
ner,
ner_converter])
data = spark.createDataFrame([["""La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales."""]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d","es")
.setInputCols(Array("sentence","token"))
.setOutputCol("embeddings")
val ner_model = MedicalNerModel.pretrained("ner_eu_clinical_condition", "es", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(documenter, sentenceDetector, tokenizer, word_embeddings, ner_model, ner_converter))
val data = Seq(Array("""La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.""")).toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+--------------------+------------------+
|chunk |ner_label |
+--------------------+------------------+
|cicatriz |clinical_condition|
|dolor a la palpación|clinical_condition|
|signos |clinical_condition|
|irritación |clinical_condition|
|hernias inguinales |clinical_condition|
Model Information
| Model Name: | ner_eu_clinical_condition |
| Compatibility: | Healthcare NLP 4.2.8+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | es |
| Size: | 898.1 KB |
References
The corpus used for model training is provided by European Clinical Case Corpus (E3C), a project aimed at offering a freely available multilingual corpus of semantically annotated clinical narratives.
Benchmarking
label tp fp fn total precision recall f1
clinical_condition 354.0 42.0 84.0 438.0 0.8939 0.8082 0.8489
macro - - - - - - 0.8489
micro - - - - - - 0.8489