ALTRUIST APP- FOR PATIENT IN RURAL PEOPLE DOCTOR MANAGEMENT SYSTEM

ABSTRACT: Epidemiological cohort studies are essential for determining participant risk variables for a range of outcomes. Because of recruitment and long-term follow-up, these studies are frequently expensive and time-consuming. As online patient communities frequently exchange information about their conditions, social media (SM) data has become an important supplementary source for digital epidemiology and health research. In this work, we present ALTRUIST, an open-source Python module that makes it possible to generate VDCS on SM in a consistent manner. Data collection, preprocessing, and analysis procedures that resemble those of a conventional cohort research are made easier with ALTRUIST. To demonstrate the methodology, we present a real-world use case that focuses on diabetes. We illustrate the potential of VDCS as a crucial tool for certain research issues by utilizing SM data, which provides extensive and reasonably priced information on users' health.A web-based application called the Health Monitoring System was created with the Django framework with the goal of simplifying doctor-patient interactions and patient health management. In addition to giving clinicians the resources they need to effectively manage visits and analyze patient progress, this system allows patients to schedule appointments with physicians, track health metrics, and access their medical history. The main elements are a doctor portal for seeing patient information, controlling availability, and recording consultations, and a patient site for making appointments, submitting symptoms, and examining medical records. This system is a versatile answer to contemporary healthcare requirements because of its scalability, which permits integration with wearable technology and other health monitoring instruments. KEYWORDS: Machine learning Algorithm (ML), social media (SM), Electronics health record (EHR), Health Insurance Portability and Accountability Act (HIPAA).