Pipeline to Detect PHI for Deidentification (Augmented)

Description

This pretrained pipeline is built on the top of ner_deid_augmented model.

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How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")

text = '''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 to be admitted to the Day Hospital for cystectomy. 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.'''

result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")

val text = "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 to be admitted to the Day Hospital for cystectomy. 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."

val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.deid.ner_augmented.pipeline").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 to be admitted to the Day Hospital for cystectomy. 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

|    | ner_chunks      |   begin |   end | ner_label   |   confidence |
|---:|:----------------|--------:|------:|:------------|-------------:|
|  0 | Smith           |      32 |    36 | NAME        |     0.9998   |
|  1 | VA Hospital     |     184 |   194 | LOCATION    |     0.98755  |
|  2 | Day Hospital    |     258 |   269 | LOCATION    |     0.9907   |
|  3 | 02/04/2003      |     341 |   350 | DATE        |     0.8956   |
|  4 | Smith           |     374 |   378 | NAME        |     0.9997   |
|  5 | Day Hospital    |     397 |   408 | LOCATION    |     0.9539   |
|  6 | Smith           |     782 |   786 | NAME        |     0.9999   |
|  7 | Smith           |    1131 |  1135 | NAME        |     0.9996   |
|  8 | 7 Ardmore Tower |    1153 |  1167 | LOCATION    |     0.784167 |
|  9 | Hart            |    1221 |  1224 | NAME        |     0.9996   |
| 10 | Smith           |    1231 |  1235 | NAME        |     0.9998   |
| 11 | 02/07/2003      |    1329 |  1338 | DATE        |     0.9997   |

Model Information

Model Name: ner_deid_augmented_pipeline
Type: pipeline
Compatibility: Healthcare NLP 4.3.0+
License: Licensed
Edition: Official
Language: en
Size: 1.7 GB

Included Models

  • DocumentAssembler
  • SentenceDetectorDLModel
  • TokenizerModel
  • WordEmbeddingsModel
  • MedicalNerModel
  • NerConverterInternalModel