Description
This pipeline can be used to de-identify 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, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, SSN, ACCOUNT, DLN, PLATE, VIN, LICENSE entities.
This pipeline simultaneously produces masked with entity labels, fixed-length char, same-length char and obfuscated version of the text.
Predicted Entities
LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, SSN, ACCOUNT, DLN, PLATE, VIN, LICENSE
How to use
from sparknlp.pretrained import PretrainedPipeline
deid_pipeline = PretrainedPipeline("clinical_deidentification_multi_mode_output", "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_multi_mode_output", "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: <DEVICE>, IP <IPADDR>.
He is a <AGE>-year-old male was admitted to the <HOSPITAL> 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 : Marlana Salvage, Record date: 2093-02-23, MR # 824235.
Dr. Vic Blackbird, ID: X2814358, IP 001.001.001.001.
He is a 68-year-old male was admitted to the PRAIRIE SAINT JOHN'S for cystectomy on 02/23/93.
Patient's VIN : 3IRWE31VQMG867619, SSN #509-32-6712, Driver's license no: W580998P.
Phone (382) 505-3976, 521 Adams St, Port Shannon, E-MAIL: Dasha@yahoo.com.
Model Information
| Model Name: | clinical_deidentification_multi_mode_output |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.3.1+ |
| 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