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, ID, LOCATION, NAME, PROFESSION, CITY, COUNTRY, DOCTOR, HOSPITAL, IDNUM, MEDICALRECORD, ORGANIZATION, PATIENT, PHONE, PROFESSION, STREET, USERNAME, ZIP, ACCOUNT, LICENSE, VIN, SSN, DLN, PLATE, IPADDR, EMAIL entities
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
deid_pipeline = PretrainedPipeline("clinical_deidentification", "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","en","clinical/models")
val 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."""
val result =deid_pipeline .annotate(sample)
import nlu
nlu.load("en.de_identify.clinical_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 Phenes, Record date: 2093-03-14, # Y5003067.
Dr. Dr Gaston Margo, IDOX:8976967, IP 001.001.001.001.
He is a 91 male was admitted to the MADONNA REHABILITATION HOSPITAL for cystectomy on 07-22-1994.
Patient's VIN : 5eeee44ffff555666, SSN 999-84-3686, Driver's license S99956482.
Phone 74 617 042, 1407 west stassney lane, Edmonton, E-MAIL: Carliss@hotmail.com.
Model Information
| Model Name: | clinical_deidentification |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
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