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  • John Snow Labs
  • Jan 18, 2022

Detect PHI for Deidentification purposes (Spanish, reduced entities)

  • deid
  • es
  • licensed

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 7 entities. This NER model is trained with a combination of custom datasets, Spanish 2002 conLL, MeddoProf dataset and several data augmentation mechanisms and it’s a reduced version of ner_deid_subentity.

Predicted Entities

CONTACT, NAME, DATE, ID, LOCATION, PROFESSION, AGE

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", "es", "clinical/models")\
	.setInputCols(["sentence","token","word_embeddings"])\
	.setOutputCol("ner")

nlpPipeline = nlp.Pipeline(stages=[
	documentAssembler,
	sentenceDetector,
	tokenizer,
	embeddings,
	clinical_ner])

text = ['''
Antonio Pérez Juan, nacido en Cadiz, España. Aún no estaba vacunado, se infectó con Covid-19 el dia 14/03/2020 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", "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 Pérez Juan, nacido en Cadiz, España. Aún no estaba vacunado, se infectó con Covid-19 el dia 14/03/2020 y tuvo que ir al Hospital. Fue tratado con anticuerpos monoclonales en la Clinica San Carlos."""

val df = Seq(text).toDS.toDF("text")

val results = pipeline.fit(df).transform(df)

import nlu
nlu.load("es.med_ner.deid.generic").predict("""
Antonio Pérez Juan, nacido en Cadiz, España. Aún no estaba vacunado, se infectó con Covid-19 el dia 14/03/2020 y tuvo que ir al Hospital. Fue tratado con anticuerpos monoclonales en la Clinica San Carlos.
""")

Results

+------------+----------+
|       token| ner_label|
+------------+----------+
|     Antonio|    B-NAME|
|       Pérez|    I-NAME|
|        Juan|    I-NAME|
|           ,|         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|         O|
|          el|         O|
|         dia|         O|
|  14/03/2020|    B-DATE|
|           y|         O|
|        tuvo|         O|
|         que|         O|
|          ir|         O|
|          al|         O|
|    Hospital|         O|
|         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
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
Dependencies: embeddings_sciwiki_300d

Data Source

Benchmarking

label      tp     fp     fn   total  precision  recall      f1
CONTACT   166.0    9.0    8.0   174.0     0.9486   0.954  0.9513
NAME  2879.0  195.0  191.0  3070.0     0.9366  0.9378  0.9372
DATE  1839.0   29.0   18.0  1857.0     0.9845  0.9903  0.9874
ID   119.0   11.0   12.0   131.0     0.9154  0.9084  0.9119
LOCATION  5149.0  711.0  607.0  5756.0     0.8787  0.8945  0.8865
PROFESSION   236.0   49.0  168.0   404.0     0.8281  0.5842  0.6851
AGE   313.0   33.0   50.0   363.0     0.9046  0.8623  0.8829
macro     -      -      -       -         -       -     0.891749
micro     -      -      -       -         -       -     0.909897