# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 1 Introduction  
The data revolution has experienced a significant transformation of the global environment, a phenomenon particularly underscored by the introduction of electronic chips in the late 1960s. This technological milestone caused a paradigm shift in data management techniques and laid the foundations for the ultimate transition in several industries, including healthcare, from analog documentation to digital records [1]. EHRs have emerged as a vast compendium of health-related data, compiled from many sources, including clinics, hospitals, labs, and other healthcare facilities. This development improved clinicalworkflows and patient care delivery by radically changing how medical information is stored and retrieved [2]. The Internet and information technology advancements have allowed health records to integrate and become more easily accessible to authorized users, beyond traditional boundaries [3]. This fluid sharing of information facilitates a comprehensive understanding of a patient's medical history, treatment plans, and results, encouraging cooperation amongst medical providers and improving the standard of patient care. In addition, the integration of health records into healthcare systems promotes evidence-based decision-making and improves administrative effectiveness, which in turn raises the standard and effectiveness of medical care [4]. Additionally, this integrated approach to health data has the potential to advance medical research, make customized treatment options possible, and stimulate innovations that could have a significant impact on the efficacy and outcomes of healthcare. The adoption of EHRs in place of traditional paper-based health records has completely changed the way data are accessed, integrated, and shared among healthcare providers, where it offers many advantages, such as better clinical decision-making, increased care coordination, and reduced administrative responsibilities [5]. These electronic databases include patient demographics, medical histories, the results of diagnostic tests, treatment plans, and progress notes, providing an in-depth understanding of a patient's status [6]. This extensive and interconnected data ecosystem empowers healthcare providers to make informed decisions and deliver tailored, effective care. The ongoing progress of EHR technology and data interoperability provides substantial opportunities for improving research, public health initiatives, and healthcare delivery and enhancement of global healthcare [7]. Health-related data are sensitive and critical; therefore, protecting them is essential. The legal framework, represented by the Health Insurance Portability and Accountability Act (HIPAA), requires the safeguarding of patient privacy to avoid harm and uphold patients' rights [8]. Unauthorized access to or disclosure of health information may have serious repercussions, such as discrimination, identity theft, and reduced access to healthcare services. Securing patient data and maintaining patient-provider confidence requires strong security measures such as encryption, access controls, and regular audits [9]. Patient notes stored in electronic health records may contain vital information for medical investigations. Most medical investigators can only access de-identified notes to protect patients' confidentiality. The Health Insurance Portability and Accountability Act of 1996 in the United States defines 18 types of Protected Health Information that must be removed to de-identify patient notes [10]. De-identification can be performed using automated or manual procedures. Experts must identify and label PHI to perform manual de-identification; however, this approach is restricted by the number of people who are allowed access to identified patient notes and the possibility of human error. Automated de-identification systems, on the other hand, make use of rule-based or machine-learning techniques. Typically, rule-based systems depend on human-defined patterns expressed as lexicons and regular expressions [11].