Description
Named Entity recognition annotator allows for a generic model to be trained by utilizing a deep learning algorithm (Char CNNs - BiLSTM - CRF - word embeddings) inspired by a former state-of-the-art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM, CNN. Deidentification NER (Generic-Augmented) 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 the i2b2 train set and an augmented version of the i2b2 train set. It is the version of ner_deid_generic_augmented model augmented with langtest library.
| test_type | before fail_count | after fail_count | before pass_count | after pass_count | minimum pass_rate | before pass_rate | after pass_rate |
|---|---|---|---|---|---|---|---|
| add_ocr_typo | 376 | 369 | 11927 | 11934 | 95% | 97% | 97% |
| lowercase | 1008 | 350 | 15138 | 15796 | 95% | 94% | 98% |
| swap_entities | 394 | 350 | 3687 | 3733 | 95% | 90% | 91% |
| titlecase | 437 | 352 | 16970 | 17055 | 95% | 97% | 98% |
| uppercase | 1085 | 506 | 16198 | 16777 | 95% | 94% | 97% |
| weighted average | 3300 | 1927 | 63920 | 65295 | 95% | 95.09% | 97.13% |
We stuck to the official annotation guideline (AG) for the 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
DATE, NAME, LOCATION, PROFESSION, CONTACT, AGE, ID
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector() \
.setInputCols(["document"]) \
.setOutputCol("sentence")
tokenizer = Tokenizer() \
.setInputCols(["sentence"]) \
.setOutputCol("token")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
deid_ner = MedicalNerModel.pretrained("ner_deid_generic_augmented_langtest", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk_generic")
nlpPipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
deid_ner,
ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame(pd.DataFrame({"text": ["""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone : (302) 786-5227."""]})))
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val deid_ner = MedicalNerModel.pretrained("ner_deid_generic_augmented_langtest", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk_generic")
val nlpPipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
deid_ner,
ner_converter))
val data = Seq("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone : (302) 786-5227.""").toDS.toDF("text")
val result = nlpPipeline.fit(data).transform(data)
Results
+-----------------------------+---------+
|chunk |ner_label|
+-----------------------------+---------+
|2093-01-13 |DATE |
|David Hale |NAME |
|Hendrickson, Ora |NAME |
|7194334 |ID |
|01/13/93 |DATE |
|Oliveira |NAME |
|25 |AGE |
|1-11-2000 |DATE |
|Cocke County Baptist Hospital|LOCATION |
|0295 Keats Street |LOCATION |
|(302) 786-5227 |CONTACT |
+-----------------------------+---------+
Model Information
| Model Name: | ner_deid_generic_augmented_langtest |
| Compatibility: | Healthcare NLP 5.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.7 MB |
References
A custom data set which is created from the i2b2-PHI train and the augmented version of the i2b2-PHI train set is used.
Benchmarking
label precision recall f1-score support
AGE 0.96 0.95 0.96 395
CONTACT 0.89 0.85 0.87 100
DATE 0.98 0.98 0.98 2355
ID 0.83 0.84 0.83 325
LOCATION 0.85 0.87 0.86 756
NAME 0.94 0.95 0.95 1314
PROFESSION 0.55 0.69 0.61 113
micro-avg 0.93 0.94 0.94 5358
macro-avg 0.86 0.88 0.87 5358
weighted-avg 0.93 0.94 0.94 5358