Clinical De-identification (EN)

**Overview**

The Clinical De-Identification model is designed to recognize and anonymize PHI in English-language clinical notes. It employs state-of-the-art natural language processing techniques to detect sensitive information such as patient names, addresses, medical record numbers, and other identifiers.
Once identified, the PHI is effectively masked or obfuscated, rendering the text safe for broader use while maintaining its informational integrity.

**Key Features:**
- Expertly crafted to recognize , mask or obfuscate a diverse array of PHI elements within medical texts, assuring thorough anonymization.
- The mask or obfuscation process is meticulously designed to adhere to HIPAA and other healthcare-related privacy laws, contributing to regulatory compliance and safeguarding patient information.
- Ideally suited for use in healthcare research, data analytics, and educational settings, this model facilitates the secure exploitation of clinical data, negating risks to patient privacy.
This model stands as an invaluable tool in healthcare and research domains, prioritizing patient confidentiality. It enables the ethical and compliant use of critical medical data, fostering advanced research and knowledge dissemination while rigorously maintaining data privacy and security.

**Additional Model Information**
- [Automated De-Identification, Consistent Obfuscation, and Regulatory Grade Validation of 2 Billion Patient Notes](https://www.johnsnowlabs.com/peer-reviewed-papers/)
- [Industry Use-Case Demo](https://demo.johnsnowlabs.com/healthcare/DEID_PHI_TEXT_MULTI/)
- [Full model info on John Snow Labs Models Hub](https://nlp.johnsnowlabs.com/2024/10/03/clinical_deidentification_docwise_wip_en.html)
- **Domain:** Clinical Data Privacy
- **Subdomain:** PHI Masking and Obfuscation for De-identification
- **Predictable entities:** LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP

**How to run this model:**
1. Acquire a John Snow Labs license from [Sales](mailto:[e-mail])
2. Import this listing.
3. See the attached notebook to deploy and use the model.