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
Predicted Entities
ACCOUNT, AGE, CITY, COUNTRY, DATE, DOCTOR, E-MAIL, HOSPITAL, ID, IDNUM, IPADDR, MEDICALRECORD, ORGANIZATION, PATIENT, PHONE, PLATE, PROFESSION, SEX, SSN, STREET, URL, USERNAME, ZIP
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 : Craige Perks, Record date: 2093-02-06, # R2593192.
Dr. Dr Felice Lacer, IDXO:4884578, IP 444.444.444.444.
He is a 75 male was admitted to the MADISON VALLEY MEDICAL CENTER for cystectomy on 07-01-1972.
Patient's VIN : 2BBBB11BBBB222999, SSN SSN-814-86-1962, Driver's license P055567317431.
Phone 0381-6762484, Budaörsi út 14., New brunswick, E-MAIL: Reba@google.com.
Model Information
| Model Name: | clinical_deidentification |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| 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
- ContextualParserModel
- ContextualParserModel
- ChunkMergeModel
- ChunkMergeModel
- DeIdentificationModel
- DeIdentificationModel
- DeIdentificationModel
- DeIdentificationModel
- Finisher