Clinical De-identification

**Clinical De-identification:**

The Clinical De-Identification Model is engineered to pinpoint and anonymize PHI in English-language clinical documentation. It skillfully identifies sensitive data, including patient identifiers, medical record numbers, and other confidential information. Upon detection, the PHI undergoes a sophisticated obfuscation process. This transformation alters the text to maintain its usability for research and analysis while effectively concealing patient-specific details.

**Key Features:**
- Expertly crafted to recognize and obfuscate a diverse array of PHI elements within medical texts, assuring thorough anonymization.
- The 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**
- [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/2023/07/11/clinical_deidentification_en.html)
- **Domain:** Clinical Data Privacy
- **Subdomain:** PHI Masking and De-identification
- **Predictable entities:** 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
- **Deployment Identifier:** 2. Clinical De-identification (Obfuscate)

**How to run this model:**
1. Acquire a John Snow Labs Pay As You Go (PAYG) license from [John Snow Labs](https://my.johnsnowlabs.com/).
2. Import this listing.
3. Use the attached notebook to deploy the model with **2. Clinical De-identification (Obfuscate)** as the model parameter. **Do not use the Open button on this page which appears after importing this listing. It will fail to deploy a model and does not work yet, you must use the attached notebook.**.

This model comes with optimized CPU and GPU builds. You can select which one to deploy via the notebook.

**How to obtain a PAYG license:**
1. Access [my.JohnSnowLabs.com](https://my.johnsnowlabs.com) and log in to your account. If you don't have an account, create one.
2. Go to the Get License page.
3. Switch to the PAYG Subscription tab and provide your credit card details.
4. Carefully review the End User License Agreement and the Terms and Conditions documents. If you agree, click on the Create Subscription button.
5. Once the process is complete, you will find your PAY-As-You-GO license listed on the My Subscriptions page.
6. Visit the My Subscriptions page and copy the PAYG license key by clicking on the copy icon in the License Key column.
7. Go to your Databricks notebook and paste your JSL-license into the JSL-License field in the top of the notebook. You are now ready to go!