Description
Named Entity Recognition annotators allow for a generic model to be trained by using a Deep Learning architecture (Char CNNs - BiLSTM - CRF - word embeddings) inspired on a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM,CNN.
Deidentification NER (Spanish) is a Named Entity Recognition model that annotates text to find protected health information that may need to be de-identified. It detects 8 entities (1 more than the ner_deid_generic ner model).
This NER model is trained with a combination of custom datasets, Spanish 2002 conLL, MeddoProf dataset, several data augmentation mechanisms and has been augmented with MEDDOCAN Spanish Deidentification corpus (compared to ner_deid_generic which does not include it). It’s a generalized version of ner_deid_subentity_augmented.
Predicted Entities
CONTACT, NAME, DATE, ID, LOCATION, PROFESSION, AGE, SEX
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl","xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_sciwiki_300d","es","clinical/models")\
.setInputCols(["sentence","token"])\
.setOutputCol("word_embeddings")
clinical_ner = medical.NerModel.pretrained("ner_deid_generic_augmented", "es", "clinical/models")\
.setInputCols(["sentence","token","word_embeddings"])\
.setOutputCol("ner")
nlpPipeline = nlp.Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
clinical_ner])
text = ['''
Antonio Miguel Martínez, un varón de 35 años de edad, de profesión auxiliar de enfermería y nacido en Cadiz, España. Aún no estaba vacunado, se infectó con Covid-19 el dia 14 de Marzo y tuvo que ir al Hospital. Fue tratado con anticuerpos monoclonales en la Clinica San Carlos.
''']
df = spark.createDataFrame([text]).toDF("text")
results = nlpPipeline.fit(df).transform(df)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl","xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("embeddings_sciwiki_300d","es","clinical/models")
.setInputCols(Array("sentence","token"))
.setOutputCol("word_embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_deid_generic_augmented", "es", "clinical/models")
.setInputCols(Array("sentence","token","word_embeddings"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
clinical_ner))
val text = "Antonio Miguel Martínez, un varón de 35 años de edad, de profesión auxiliar de enfermería y nacido en Cadiz, España. Aún no estaba vacunado, se infectó con Covid-19 el dia 14 de Marzo y tuvo que ir al Hospital. Fue tratado con anticuerpos monoclonales en la Clinica San Carlos."
val df = Seq(text).toDF("text")
val results = pipeline.fit(df).transform(df)
import nlu
nlu.load("es.med_ner.deid.generic_augmented").predict("""
Antonio Miguel Martínez, un varón de 35 años de edad, de profesión auxiliar de enfermería y nacido en Cadiz, España. Aún no estaba vacunado, se infectó con Covid-19 el dia 14 de Marzo y tuvo que ir al Hospital. Fue tratado con anticuerpos monoclonales en la Clinica San Carlos.
""")
Results
+------------+------------+
| token| ner_label|
+------------+------------+
| Antonio| B-NAME|
| Miguel| I-NAME|
| Martínez| I-NAME|
| ,| O|
| un| B-SEX|
| varón| I-SEX|
| de| O|
| 35| B-AGE|
| años| O|
| de| O|
| edad| O|
| ,| O|
| de| O|
| profesión| O|
| auxiliar|B-PROFESSION|
| de|I-PROFESSION|
| enfermería|I-PROFESSION|
| y| O|
| nacido| O|
| en| O|
| Cadiz| B-LOCATION|
| ,| O|
| España| B-LOCATION|
| .| O|
| Aún| O|
| no| O|
| estaba| O|
| vacunado| O|
| ,| O|
| se| O|
| infectó| O|
| con| O|
| Covid-19|B-PROFESSION|
| el| O|
| dia| O|
| 14| O|
| de| O|
| Marzo| O|
| y| O|
| tuvo| O|
| que| O|
| ir| O|
| al| O|
| Hospital| B-LOCATION|
| Fue| O|
| tratado| O|
| con| O|
| anticuerpos| O|
|monoclonales| O|
| en| O|
| la| O|
| Clinica| B-LOCATION|
| San| I-LOCATION|
| Carlos| I-LOCATION|
| .| O|
+------------+------------+
Model Information
| Model Name: | ner_deid_generic_augmented |
| Compatibility: | Healthcare NLP 3.3.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, word_embeddings] |
| Output Labels: | [ner] |
| Language: | es |
| Size: | 15.0 MB |
References
- Internal JSL annotated corpus
- Spanish conLL
- MeddoProf
- MeddoCan
Benchmarking
label tp fp fn total precision recall f1
CONTACT 185.0 3.0 0.0 185.0 0.984 1.0 0.992
NAME 2066.0 138.0 106.0 2172.0 0.9374 0.9512 0.9442
DATE 1017.0 18.0 18.0 1035.0 0.9826 0.9826 0.9826
ORGANIZATION 2468.0 482.0 332.0 2800.0 0.8366 0.8814 0.8584
ID 65.0 5.0 3.0 68.0 0.9286 0.9559 0.942
SEX 678.0 8.0 15.0 693.0 0.9883 0.9784 0.9833
LOCATION 2532.0 358.0 420.0 2952.0 0.8761 0.8577 0.8668
PROFESSION 246.0 9.0 31.0 277.0 0.9647 0.8881 0.9248
AGE 547.0 8.0 9.0 556.0 0.9856 0.9838 0.9847
macro - - - - - - 0.9421
micro - - - - - - 0.9092