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 on a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM,CNN.Deidentification NER (Enriched) is a Named Entity Recognition model that annotates text to find protected health information that may need to be deidentified. The entities it annotates are Age, City, Country, Date, Doctor, Hospital, Idnum, Medicalrecord, Organization, Patient, Phone, Profession, State, Street, Username, and Zip. Clinical NER is trained with the ‘embeddings_clinical’ word embeddings model, so be sure to use the same embeddings in the pipeline.
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
AGE, CITY, COUNTRY, DATE, DOCTOR, HOSPITAL, IDNUM, MEDICALRECORD, ORGANIZATION, PATIENT, PHONE, PROFESSION, STATE, STREET, 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_deid_enriched","en","clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner,
ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([['HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003. HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.']], ["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_deid_enriched","en","clinical/models")
.setInputCols("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("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003. HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""").toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.deid.enriched").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003. HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
Results
+---------------+---------+
|chunk |ner_label|
+---------------+---------+
|Smith |PATIENT |
|VA Hospital |HOSPITAL |
|Day Hospital |HOSPITAL |
|02/04/2003 |DATE |
|Smith |PATIENT |
|Day Hospital |HOSPITAL |
|Smith |PATIENT |
|Smith |PATIENT |
|7 Ardmore Tower|HOSPITAL |
|Hart |DOCTOR |
|Smith |PATIENT |
|02/07/2003 |DATE |
+---------------+---------+
Model Information
| Model Name: | ner_deid_enriched |
| 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
I-AGE 7 3 6 0.7 0.538462 0.608696
I-DOCTOR 800 27 94 0.967352 0.894855 0.929692
I-IDNUM 6 0 2 1 0.75 0.857143
B-DATE 1883 34 56 0.982264 0.971119 0.97666
I-DATE 425 28 25 0.93819 0.944444 0.941307
B-PHONE 29 7 9 0.805556 0.763158 0.783784
B-STATE 87 4 11 0.956044 0.887755 0.920635
B-CITY 35 11 26 0.76087 0.57377 0.654206
I-ORGANIZATION 12 4 15 0.75 0.444444 0.55814
B-DOCTOR 728 75 53 0.9066 0.932138 0.919192
I-PROFESSION 43 11 13 0.796296 0.767857 0.781818
I-PHONE 62 4 4 0.939394 0.939394 0.939394
B-AGE 234 13 16 0.947368 0.936 0.94165
B-STREET 20 7 16 0.740741 0.555556 0.634921
I-ZIP 60 3 2 0.952381 0.967742 0.96
I-MEDICALRECORD 54 5 2 0.915254 0.964286 0.93913
B-ZIP 2 1 0 0.666667 1 0.8
B-HOSPITAL 256 23 66 0.917563 0.795031 0.851913
I-STREET 150 17 20 0.898204 0.882353 0.890208
B-COUNTRY 22 2 8 0.916667 0.733333 0.814815
I-COUNTRY 1 0 0 1 1 1
I-STATE 6 0 1 1 0.857143 0.923077
B-USERNAME 30 0 4 1 0.882353 0.9375
I-HOSPITAL 295 37 64 0.888554 0.821727 0.853835
I-PATIENT 243 26 41 0.903346 0.855634 0.878843
B-PROFESSION 52 8 17 0.866667 0.753623 0.806202
B-IDNUM 32 3 12 0.914286 0.727273 0.810127
I-CITY 76 15 13 0.835165 0.853933 0.844444
B-PATIENT 337 29 40 0.920765 0.893899 0.907133
B-MEDICALRECORD 74 6 4 0.925 0.948718 0.936709
B-ORGANIZATION 20 5 13 0.8 0.606061 0.689655
Macro-average 6083 408 673 0.7976 0.697533 0.744218
Micro-average 6083 408 673 0.937144 0.900385 0.918397