Description
Named Entity Recognition annotator (NERDLModel) allows for a generic model to be trained by utilizing a deep learning algorithm (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 (DL) is a Named Entity Recognition model that annotates text to find protected health information that may need to be deidentified.
Predicted Entities
AGE,BIOID,CITY,COUNTRY,DATE,DEVICE,DOCTOR,EMAIL,HOSPITAL,FAX,HEALTHPLAN,HOSPITAL,IDNUM,LOCATION-OTHER,MEDICALRECORD,ORGANIZATION,PATIENT,PHONE,PROFESSION,STATE,STREET,URL,USERNAME,ZIP
Live Demo Open in Colab Copy S3 URI
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")
ner = MedicalNerModel.pretrained("ner_deidentify_dl","en","clinical/models") \
.setInputCols("sentence","token","embeddings") \
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlp_pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner,
ner_converter])
model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["A . Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson , Ora MR . # 7194334 Date : 01/13/93 PCP : Oliveira , 25 month years-old , Record date : 2079-11-09 . Cocke County Baptist Hospital . 0295 Keats Street"]], ["text"]))
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 ner = MedicalNerModel.pretrained("ner_deidentify_dl","en","clinical/models")
.setInputCols(Array("sentence","token","embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
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 month years-old , Record date : 2079-11-09 . Cocke County Baptist Hospital . 0295 Keats Street""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.deid").predict("""A . Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson , Ora MR . # 7194334 Date : 01/13/93 PCP : Oliveira , 25 month years-old , Record date : 2079-11-09 . Cocke County Baptist Hospital . 0295 Keats Street""")
Results
+---------------+-----+
|ner_label |count|
+---------------+-----+
|O |28 |
|I-HOSPITAL |4 |
|B-DATE |3 |
|I-STREET |3 |
|I-PATIENT |2 |
|B-DOCTOR |2 |
|B-AGE |1 |
|B-PATIENT |1 |
|I-DOCTOR |1 |
|B-MEDICALRECORD|1 |
+---------------+-----+.
+-----------------------------+-------------+
|chunk |ner_label |
+-----------------------------+-------------+
|2093-01-13 |DATE |
|David Hale |DOCTOR |
|Hendrickson , Ora |PATIENT |
|7194334 |MEDICALRECORD|
|01/13/93 |DATE |
|Oliveira |DOCTOR |
|25 |AGE |
|2079-11-09 |DATE |
|Cocke County Baptist Hospital|HOSPITAL |
|0295 Keats Street |STREET |
+-----------------------------+-------------+
Model Information
| Model Name: | ner_deidentify_dl |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
Trained on JSL enriched n2c2 2014: De-identification and Heart Disease Risk Factors Challenge datasets with embeddings_clinical https://portal.dbmi.hms.harvard.edu/projects/n2c2-2014/
Benchmarking
| | label | tp | fp | fn | prec | rec | f1 |
|---:|:-----------------|------:|-----:|-----:|---------:|---------:|---------:|
| 1 | I-AGE | 7 | 3 | 6 | 0.7 | 0.538462 | 0.608696 |
| 2 | I-DOCTOR | 800 | 27 | 94 | 0.967352 | 0.894855 | 0.929692 |
| 3 | I-IDNUM | 6 | 0 | 2 | 1 | 0.75 | 0.857143 |
| 4 | B-DATE | 1883 | 34 | 56 | 0.982264 | 0.971119 | 0.97666 |
| 5 | I-DATE | 425 | 28 | 25 | 0.93819 | 0.944444 | 0.941307 |
| 6 | B-PHONE | 29 | 7 | 9 | 0.805556 | 0.763158 | 0.783784 |
| 7 | B-STATE | 87 | 4 | 11 | 0.956044 | 0.887755 | 0.920635 |
| 8 | B-CITY | 35 | 11 | 26 | 0.76087 | 0.57377 | 0.654206 |
| 9 | I-ORGANIZATION | 12 | 4 | 15 | 0.75 | 0.444444 | 0.55814 |
| 10 | B-DOCTOR | 728 | 75 | 53 | 0.9066 | 0.932138 | 0.919192 |
| 11 | I-PROFESSION | 43 | 11 | 13 | 0.796296 | 0.767857 | 0.781818 |
| 12 | I-PHONE | 62 | 4 | 4 | 0.939394 | 0.939394 | 0.939394 |
| 13 | B-AGE | 234 | 13 | 16 | 0.947368 | 0.936 | 0.94165 |
| 14 | B-STREET | 20 | 7 | 16 | 0.740741 | 0.555556 | 0.634921 |
| 15 | I-ZIP | 60 | 3 | 2 | 0.952381 | 0.967742 | 0.96 |
| 16 | I-MEDICALRECORD | 54 | 5 | 2 | 0.915254 | 0.964286 | 0.93913 |
| 17 | B-ZIP | 2 | 1 | 0 | 0.666667 | 1 | 0.8 |
| 18 | B-HOSPITAL | 256 | 23 | 66 | 0.917563 | 0.795031 | 0.851913 |
| 19 | I-STREET | 150 | 17 | 20 | 0.898204 | 0.882353 | 0.890208 |
| 20 | B-COUNTRY | 22 | 2 | 8 | 0.916667 | 0.733333 | 0.814815 |
| 21 | I-COUNTRY | 1 | 0 | 0 | 1 | 1 | 1 |
| 22 | I-STATE | 6 | 0 | 1 | 1 | 0.857143 | 0.923077 |
| 23 | B-USERNAME | 30 | 0 | 4 | 1 | 0.882353 | 0.9375 |
| 24 | I-HOSPITAL | 295 | 37 | 64 | 0.888554 | 0.821727 | 0.853835 |
| 25 | I-PATIENT | 243 | 26 | 41 | 0.903346 | 0.855634 | 0.878843 |
| 26 | B-PROFESSION | 52 | 8 | 17 | 0.866667 | 0.753623 | 0.806202 |
| 27 | B-IDNUM | 32 | 3 | 12 | 0.914286 | 0.727273 | 0.810127 |
| 28 | I-CITY | 76 | 15 | 13 | 0.835165 | 0.853933 | 0.844444 |
| 29 | B-PATIENT | 337 | 29 | 40 | 0.920765 | 0.893899 | 0.907133 |
| 30 | B-MEDICALRECORD | 74 | 6 | 4 | 0.925 | 0.948718 | 0.936709 |
| 31 | B-ORGANIZATION | 20 | 5 | 13 | 0.8 | 0.606061 | 0.689655 |
| 32 | Macro-average | 6083 | 408 | 673 | 0.7976 | 0.697533 | 0.744218 |
| 33 | Micro-average | 6083 | 408 | 673 | 0.937144 | 0.900385 | 0.918397 |