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_roberta 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_roberta which does not include it). It’s a generalized version of ner_deid_subentity_roberta_augmented.
This is a Roberta embeddings based model. You also have available the ner_deid_generic_augmented that uses Sciwi 300d embeddings.
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")
roberta_embeddings = nlp.RoBertaEmbeddings.pretrained("roberta_base_biomedical", "es")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
clinical_ner = medical.NerModel.pretrained("ner_deid_generic_roberta_augmented", "es", "clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner")
nlpPipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
roberta_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")
roberta_embeddings = RoBertaEmbeddings.pretrained("roberta_base_biomedical", "es")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_deid_generic_augmented", "es", "clinical/models")
.setInputCols(Array("sentence","token","embeddings"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
sentenceDetector,
tokenizer,
roberta_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.roberta").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_roberta_augmented |
| Compatibility: | Healthcare NLP 3.3.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | es |
| Size: | 16.3 MB |
References
- Internal JSL annotated corpus
- Spanish conLL
- MeddoProf
- MeddoCan
Benchmarking
label tp fp fn total precision recall f1
CONTACT 177.0 3.0 6.0 183.0 0.9833 0.9672 0.9752
NAME 1963.0 159.0 123.0 2086.0 0.9251 0.941 0.933
DATE 953.0 18.0 16.0 969.0 0.9815 0.9835 0.9825
ORGANIZATION 2320.0 520.0 362.0 2682.0 0.8169 0.865 0.8403
ID 63.0 7.0 1.0 64.0 0.9 0.9844 0.9403
SEX 619.0 14.0 8.0 627.0 0.9779 0.9872 0.9825
LOCATION 2388.0 470.0 423.0 2811.0 0.8355 0.8495 0.8425
PROFESSION 233.0 15.0 28.0 261.0 0.9395 0.8927 0.9155
AGE 516.0 16.0 3.0 519.0 0.9699 0.9942 0.9819
macro - - - - - - 0.93263
micro - - - - - - 0.89427