Description
This pipeline 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 AGE, CONTACT, DATE, LOCATION, NAME, PROFESSION, CITY, COUNTRY, DOCTOR, HOSPITAL, IDNUM, MEDICALRECORD, ORGANIZATION, PATIENT, PHONE, STREET, USERNAME, ZIP, ACCOUNT, LICENSE, VIN, SSN, DLN, PLATE, IPADDR entities.
Predicted Entities
ACCOUNT, AGE, BIOID, CITY, CONTACT, COUNTRY, DATE, DEVICE, DLN, DOCTOR, EMAIL, FAX, HEALTHPLAN, HOSPITAL, ID, IDNUM, LICENSE, LOCATION, LOCATION-OTHER, MEDICALRECORD, NAME, ORGANIZATION, PATIENT, PHONE, PLATE, PROFESSION, SSN, STATE, STREET, URL, USERNAME, VIN, ZIP
How to use
from sparknlp.pretrained import PretrainedPipeline
deid_pipeline = PretrainedPipeline("clinical_deidentification", "en", "clinical/models")
text = """Name : Hendrickson, Ora, Record date: 2093-01-13, MR #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(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val deid_pipeline = PretrainedPipeline("clinical_deidentification", "en", "clinical/models")
val text = """Name : Hendrickson, Ora, Record date: 2093-01-13, MR #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."""
val result = deid_pipeline.annotate(text)
Results
print("\nMasked with entity labels")
print("-"*30)
print("\n".join(result['masked']))
print("\nMasked with chars")
print("-"*30)
print("\n".join(result['masked_with_chars']))
print("\nMasked with fixed length chars")
print("-"*30)
print("\n".join(result['masked_fixed_length_chars']))
print("\nObfuscated")
print("-"*30)
print("\n".join(result['obfuscated']))
Masked with entity labels
------------------------------
Name : <PATIENT>, Record date: <DATE>, MR <MEDICALRECORD>.
Dr. <DOCTOR>, ID<IDNUM>, IP <IPADDR>.
He is a <AGE>-year-old male was admitted to the <LOCATION> for cystectomy on <DATE>.
Patient's VIN : <VIN>, SSN <SSN>, Driver's license no: <DLN>.
Phone <PHONE>, <STREET>, <CITY>, E-MAIL: <EMAIL>.
Masked with chars
------------------------------
Name : [**************], Record date: [********], MR [*****].
Dr. [********], ID[**********], IP [************].
He is a **-year-old male was admitted to the [**********] for cystectomy on [******].
Patient's VIN : [***************], SSN [**********], Driver's license no: [******].
Phone [************], [***************], [***********], E-MAIL: [*************].
Masked with fixed length chars
------------------------------
Name : ****, Record date: ****, MR ****.
Dr. ****, ID****, IP ****.
He is a ****-year-old male was admitted to the **** for cystectomy on ****.
Patient's VIN : ****, SSN ****, Driver's license no: ****.
Phone ****, ****, ****, E-MAIL: ****.
Obfuscated
------------------------------
Name : Devonne Doughty, Record date: 2093-02-22, MR # 324401.
Dr. Lennette Bihari, ID: P168558, IP 003.003.003.003.
He is a 73-year-old male was admitted to the MEMORIAL HOSPITAL & PHYSICIAN GROUP for cystectomy on 02/22/93.
Patient's VIN : 0UVOZ36UYQI347425, SSN #956-38-7564, Driver's license no: P329518A.
Phone (416) 606-3016, 300 East 8Th St, Pierre, E-MAIL: Marcello@google.com.
Model Information
| Model Name: | clinical_deidentification |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.2.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- MedicalNerModel
- NerConverter
- ChunkMergeModel
- ContextualParserModel
- ContextualParserModel
- ContextualParserModel
- ContextualParserModel
- ContextualParserModel
- ContextualParserModel
- TextMatcherModel
- ContextualParserModel
- RegexMatcherModel
- ContextualParserModel
- ContextualParserModel
- ContextualParserModel
- ContextualParserModel
- ChunkMergeModel
- ChunkMergeModel
- DeIdentificationModel
- DeIdentificationModel
- DeIdentificationModel
- DeIdentificationModel
- Finisher