Virtual Health Assistance

Title: Combating Healthcare Fraud with Autoencoder + GMM Anomaly Detection Problem Statement: Fraudulent claims and billing practices within the healthcare industry pose a significant threat to financial resources and the overall integrity of the system. These illicit activities, such as falsified medical records, unnecessary services, kickbacks, and identity theft, result in the misappropriation of funds intended for legitimate medical expenses. Detecting and preventing such fraudulent activities is crucial to safeguarding healthcare finances and ensuring that resources are allocated appropriately for patient care and medical advancements. Why is the problem relevant? The healthcare industry is a vital sector that impacts the well-being of millions worldwide. Fraudulent activities not only drain valuable financial resources but also undermine the trust of patients and stakeholders in the healthcare system. By addressing this problem, healthcare organizations can protect their financia...

Title: Revolutionizing Healthcare with RAG-based Virtual Health Assistance Using Mistral 7B and Chroma DB Introduction: The healthcare industry is constantly evolving, and advancements in technology have been playing a pivotal role in improving patient care and streamlining operations. One such innovative approach is the integration of Retrieval-Augmented Generation (RAG) models with large language models like Mistral 7B and state-of-the-art vector databases like Chroma DB. This powerful combination has the potential to revolutionize virtual health assistance, providing patients and healthcare professionals with a more personalized, efficient, and accurate experience. What is Retrieval-Augmented Generation (RAG)? Retrieval-Augmented Generation (RAG) is a cutting-edge natural language processing (NLP) technique that combines the strengths of retrieval-based and generation-based models. It leverages the ability of retrieval models to fetch relevant information from a knowledge base and t...