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 (Romanian) is a Named Entity Recognition model that annotates text to find protected health information that may need to be de-identified. It is trained with word2vec embeddings and can detect 7 generic entities.
This NER model is trained with a combination of custom datasets with several data augmentation mechanisms.
Predicted Entities
AGE, CONTACT, DATE, ID, LOCATION, NAME, PROFESSION
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d","ro")\
.setInputCols(["sentence","token"])\
.setOutputCol("word_embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_deid_generic", "ro", "clinical/models")\
.setInputCols(["sentence","token","word_embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
clinical_ner,
ner_converter])
text = """ Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
Tel: +40(235)413773
Data setului de analize: 25 May 2022 15:36:00
Nume si Prenume : BUREAN MARIA, Varsta: 77
Medic : Agota Evelyn Tımar
C.N.P : 2450502264401"""
data = spark.createDataFrame([[text]]).toDF("text")
results = nlpPipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = new SentenceDetector()
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d","ro")
.setInputCols(Array("sentence","token"))
.setOutputCol("word_embeddings")
val clinical_ner = MedicalNerModel.pretrained("ner_deid_generic", "ro", "clinical/models")
.setInputCols(Array("sentence","token","word_embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
clinical_ner,
ner_converter))
val text = """Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
Tel: +40(235)413773
Data setului de analize: 25 May 2022 15:36:00
Nume si Prenume : BUREAN MARIA, Varsta: 77
Medic : Agota Evelyn Tımar
C.N.P : 2450502264401"""
val data = Seq(text).toDS.toDF("text")
val results = pipeline.fit(data).transform(data)
import nlu
nlu.load("ro.med_ner.deid_generic").predict(""" Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
Tel: +40(235)413773
Data setului de analize: 25 May 2022 15:36:00
Nume si Prenume : BUREAN MARIA, Varsta: 77
Medic : Agota Evelyn Tımar
C.N.P : 2450502264401""")
Results
+----------------------------+---------+
|chunk |ner_label|
+----------------------------+---------+
|Spitalul Pentru Ochi de Deal|LOCATION |
|Drumul Oprea Nr. |LOCATION |
|Vaslui |LOCATION |
|737405 România |LOCATION |
|+40(235)413773 |CONTACT |
|25 May 2022 |DATE |
|BUREAN MARIA |NAME |
|77 |AGE |
|Agota Evelyn Tımar |NAME |
|2450502264401 |ID |
+----------------------------+---------+
Model Information
| Model Name: | ner_deid_generic |
| Compatibility: | Healthcare NLP 3.5.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | ro |
| Size: | 15.0 MB |
References
- Custom John Snow Labs datasets
- Data augmentation techniques
Benchmarking
label precision recall f1-score support
AGE 0.96 0.98 0.97 1235
CONTACT 0.98 0.99 0.99 379
DATE 0.90 0.85 0.87 5006
ID 1.00 1.00 1.00 694
LOCATION 0.89 0.90 0.90 1746
NAME 0.95 0.98 0.96 3018
PROFESSION 0.78 0.72 0.75 173
micro-avg 0.93 0.91 0.92 12251
macro-avg 0.92 0.92 0.92 12251
weighted-avg 0.92 0.91 0.92 12251