Detect PHI for Deidentification purposes (Spanish, reduced entities, augmented data)

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

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