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 (Arabic) is a Named Entity Recognition model that annotates text to find protected health information that may need to be de-identified. It detects 17 entities. This NER model is trained with a combination of custom datasets, and several data augmentation mechanisms. This model Word2Vec Arabic Clinical Embeddings.
Predicted Entities
PATIENT, HOSPITAL, DATE, ORGANIZATION, CITY, STREET, USERNAME, SEX, IDNUM, EMAIL, ZIP, MEDICALRECORD, PROFESSION, PHONE, COUNTRY, DOCTOR, AGE
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("arabic_w2v_cc_300d", "ar")\
.setInputCols(["document", "token"])\
.setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_deid_subentity", "ar", "clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
clinical_ner,
ner_converter])
text = '''
عالج الدكتور محمد المريض أحمد البالغ من العمر 55 سنة في 15/05/2000 في مستشفى مدينة الرباط. رقم هاتفه هو 0610948235 وبريده الإلكتروني
mohamedmell@gmail.com.
'''
data = spark.createDataFrame([[text]]).toDF("text")
results = nlpPipeline .fit(data).transform(data)
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")
val embeddings = WordEmbeddingsModel.pretrained("arabic_w2v_cc_300d", "ar")
.setInputCols(Array("sentence","token"))
.setOutputCol("word_embeddings")
val clinical_ner = MedicalNerModel.pretrained("ner_deid_subentity", "ar", "clinical/models")
.setInputCols(Array("sentence","token","word_embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
clinical_ner,
ner_converter))
text = '''
عالج الدكتور محمد المريض أحمد البالغ من العمر 55 سنة في 15/05/2000 في مستشفى مدينة الرباط. رقم هاتفه هو 0610948235 وبريده الإلكتروني
mohamedmell@gmail.com.
'''
val data = Seq(text).toDS.toDF("text")
val results = pipeline.fit(data).transform(data)
Results
+------------------------------------------------+----------------+
|chunk |ner_label|
+------------------------------------------------+---------------+
|محمد |DOCTOR |
|55 سنة |AGE |
|15/05/2000 |DATE |
|الرباط |CITY |
|0610948235 |PHONE |
|mohamedmell@gmail.com |EMAIL |
+------------------------------------------------+--------------+
Model Information
| Model Name: | ner_deid_subentity |
| Compatibility: | Healthcare NLP 4.4.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | ar |
| Size: | 15.0 MB |
References
Custom John Snow Labs datasets
Data augmentation techniques
Benchmarking
label tp fp fn total precision recall f1
PATIENT 196.0 26.0 32.0 228.0 0.8829 0.8596 0.8711
HOSPITAL 193.0 41.0 37.0 230.0 0.8248 0.8391 0.8319
DATE 877.0 14.0 8.0 885.0 0.9843 0.991 0.9876
ORGANIZATION 41.0 11.0 6.0 47.0 0.7885 0.8723 0.8283
CITY 260.0 8.0 5.0 265.0 0.9701 0.9811 0.9756
STREET 103.0 3.0 0.0 103.0 0.9717 1.0 0.9856
USERNAME 8.0 0.0 0.0 8.0 1.0 1.0 1.0
SEX 300.0 9.0 69.0 369.0 0.9709 0.813 0.885
IDNUM 13.0 1.0 0.0 13.0 0.9286 1.0 0.963
EMAIL 112.0 5.0 0.0 112.0 0.9573 1.0 0.9782
ZIP 80.0 4.0 0.0 80.0 0.9524 1.0 0.9756
MEDICALRECORD 17.0 1.0 0.0 17.0 0.9444 1.0 0.9714
PROFESSION 303.0 27.0 32.0 335.0 0.9182 0.9045 0.9113
PHONE 38.0 4.0 2.0 40.0 0.9048 0.95 0.9268
COUNTRY 158.0 10.0 8.0 166.0 0.9405 0.9518 0.9461
DOCTOR 440.0 23.0 34.0 474.0 0.9503 0.9283 0.9392
AGE 610.0 18.0 7.0 617.0 0.9713 0.9887 0.9799
macro - - - - - - 0.9386
micro - - - - - - 0.9434