A SURVEY ON ENHANCING HEALTHCARE SECURITY: INTEGRATING NLP, DEEP LEARNING, AND BLOCKCHAIN TECHNOLOGIES

Abstract

The healthcare ecosystem comprises various types of sensitive data, from patient records and clinical notes to diagnostic results and other private information protected by HIPAA. Even though traditional solutions can effectively address most security challenges, they often lack in their capabilities. Robust encryption techniques are developed with deep learning algorithms. These algorithms use their ability to detect complex patterns to identify security threats, thus making the entire healthcare system more resilient. NLP, when combined with deep learning, enables anonymization and de identification of patient data, thereby allowing sharing of data and collaboration between healthcare providers without compromising patient privacy. This is especially important not just in medical research but also in telemedicine, where the dissemination of data is critical for improved treatment and health outcomes. Natural language processing in healthcare: Techniques and methods for securing data. With the widespread adoption of NLP for processing sensitive health information, protecting patient data is paramount. To help safeguard sensitive information such methods are used: data encryption, differential privacy, tokenisation, and access control.