Clinical Deidentification (glove)

Description

This pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, URL, LOCATION-OTHER, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, SSN, ACCOUNT, DLN, PLATE, LICENSE entities.

Predicted Entities

LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, URL, LOCATION-OTHER, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, SSN, ACCOUNT, DLN, PLATE, LICENSE

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How to use

from sparknlp.pretrained import PretrainedPipeline

deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models")


sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
Dr. John Green, ID: 1231511863, IP 203.120.223.13.
He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""

result = deid_pipeline.annotate(sample)

print("\n".join(result['masked']))
print("\n".join(result['masked_with_chars']))
print("\n".join(result['masked_fixed_length_chars']))
print("\n".join(result['obfuscated']))
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models")

val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.")
import nlu
nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
Dr. John Green, ID: 1231511863, IP 203.120.223.13.
He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")

Results

Masked with entity labels
------------------------------
Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
Dr. <DOCTOR>, ID: <IDNUM>, IP <IPADDR>.
He is a <AGE> male was admitted to the <HOSPITAL> for cystectomy on <DATE>.
Patient's VIN : <VIN>, SSN <SSN>, Driver's license <DLN>.
Phone <PHONE>, <STREET>, <CITY>, E-MAIL: <EMAIL>.

Masked with chars
------------------------------
Name : [**************], Record date: [********], # [****].
Dr. [********], ID: [********], IP [************].
He is a [*********] male was admitted to the [**********] for cystectomy on [******].
Patient's VIN : [***************], SSN [**********], Driver's license [*********].
Phone [************], [***************], [***********], E-MAIL: [*************].

Masked with fixed length chars
------------------------------
Name : ****, Record date: ****, # ****.
Dr. ****, ID: ****, IP ****.
He is a **** male was admitted to the **** for cystectomy on ****.
Patient's VIN : ****, SSN ****, Driver's license ****.
Phone ****, ****, ****, E-MAIL: ****.

Obfuscated
------------------------------
Name : Berneta Anis, Record date: 2093-02-19, # U4660137.
Dr. Dr Worley Colonel, ID: ZJ:9570208, IP 005.005.005.005.
He is a 67 male was admitted to the ST. LUKE'S HOSPITAL AT THE VINTAGE for cystectomy on 06-02-1981.
Patient's VIN : 3CCCC22DDDD333888, SSN SSN-618-77-1042, Driver's license W693817528998.
Phone 0496 46 46 70, 3100 weston rd, Shattuck, E-MAIL: Freddi@hotmail.com.

Model Information

Model Name: clinical_deidentification_glove
Type: pipeline
Compatibility: Healthcare NLP 4.4.4+
License: Licensed
Edition: Official
Language: en
Size: 181.4 MB

Included Models

  • DocumentAssembler
  • SentenceDetector
  • TokenizerModel
  • WordEmbeddingsModel
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • ChunkMergeModel
  • ContextualParserModel
  • ContextualParserModel
  • ContextualParserModel
  • ContextualParserModel
  • ContextualParserModel
  • ContextualParserModel
  • ContextualParserModel
  • ContextualParserModel
  • ChunkMergeModel
  • ChunkMergeModel
  • DeIdentificationModel
  • DeIdentificationModel
  • DeIdentificationModel
  • DeIdentificationModel
  • Finisher