Description
Deidentification NER is a Named Entity Recognition model that annotates text to find protected health information that may need to be de-identified. It detects 23 entities. This ner model is trained with a combination of the i2b2 train set and a re-augmented version of i2b2 train set using BertForTokenClassification
We sticked to official annotation guideline (AG) for 2014 i2b2 Deid challenge while annotating new datasets for this model. All the details regarding the nuances and explanations for AG can be found here https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978170/
Predicted Entities
I-DATE, I-CITY, B-EMAIL, I-HOSPITAL, B-STATE, B-PHONE, B-MEDICALRECORD, B-HOSPITAL, B-USERNAME, I-ZIP, I-COUNTRY, B-ZIP, O, B-PROFESSION, I-URL, B-STREET, B-BIOID, B-URL, I-FAX, I-LOCATION-OTHER, I-AGE, I-DOCTOR, I-MEDICALRECORD, I-STATE, B-CITY, I-ORGANIZATION, B-DEVICE, I-STREET, B-COUNTRY, B-HEALTHPLAN, I-PATIENT, B-DOCTOR, B-AGE, I-PHONE, B-ORGANIZATION, I-IDNUM, I-DEVICE, B-FAX, B-LOCATION-OTHER, B-PATIENT, B-IDNUM, I-PROFESSION, B-DATE, I-HEALTHPLAN, PAD
How to use
from sparknlp.base import DocumentAssembler
from sparknlp_jsl.annotator import MedicalBertForTokenClassifier
from sparknlp.annotator import Tokenizer, NerConverter
from pyspark.ml import Pipeline
document_assembler = (
DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
)
tokenizer = (
Tokenizer()
.setInputCols(["document"])
.setOutputCol("token")
)
token_classifier = (
MedicalBertForTokenClassifier.pretrained(
"bert_token_classifier_ner_deid_onnx",
"en",
"clinical/models"
)
.setInputCols(["token", "document"])
.setOutputCol("ner")
.setCaseSensitive(True)
)
ner_converter = (
NerConverterInternal()
.setInputCols(["document", "token", "ner"])
.setOutputCol("ner_chunk")
)
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
token_classifier,
ner_converter
])
test_sentence = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
data = spark.createDataFrame([[test_sentence]]).toDF("text")
model = pipeline.fit(data)
result = model.transform(data)
from johnsnowlabs import nlp, medical
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = nlp.Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
token_classifier = medical.BertForTokenClassifier.pretrained(
"bert_token_classifier_ner_deid_onnx",
"en",
"clinical/models"
)\
.setInputCols(["token", "document"])\
.setOutputCol("ner")\
.setCaseSensitive(True)
ner_converter = medical.NerConverterInternal()\
.setInputCols(["document", "token", "ner"])\
.setOutputCol("ner_chunk")
pipeline = nlp.Pipeline(stages=[
document_assembler,
tokenizer,
token_classifier,
ner_converter
])
test_sentence = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
data = spark.createDataFrame([[test_sentence]]).toDF("text")
model = pipeline.fit(data)
result = model.transform(data)
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.annotators.ner.NerConverter
import com.johnsnowlabs.nlp.annotators.classifier.dl.MedicalBertForTokenClassifier
import org.apache.spark.ml.Pipeline
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = MedicalBertForTokenClassifier
.pretrained("bert_token_classifier_ner_deid_onnx", "en", "clinical/models")
.setInputCols(Array("token", "document"))
.setOutputCol("ner")
.setCaseSensitive(true)
val nerConverter = new NerConverterInternal()
.setInputCols(Array("document", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline()
.setStages(Array(
documentAssembler,
tokenizer,
tokenClassifier,
nerConverter
))
val testSentence = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
val data = Seq(testSentence).toDF("text")
val model = pipeline.fit(data)
val result = model.transform(data)
Results
+-----------------------------+------------+
|text |entity |
+-----------------------------+------------+
|2093-01-13 |DATE |
|David Hale |DOCTOR |
|Hendrickson, Ora |CITY |
|7194334 |IDNUM |
|Oliveira |PATIENT |
|Cocke County Baptist Hospital|HOSPITAL |
|0295 Keats Street |PHONE |
|302) 786-5227 |PHONE |
|Brothers Coal-Mine |ORGANIZATION|
+-----------------------------+------------+
Model Information
| Model Name: | bert_token_classifier_ner_deid_onnx |
| Compatibility: | Healthcare NLP 6.1.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 403.8 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |