基于自然语言处理的智能医疗诊断系统

An AI-driven healthcare assistant that predicts diseases based on user-provided symptoms. This project leverages machine learning for disease prediction, provides descriptions and precautionary measures, and includes a user-friendly GUI with text-to-speech integration.

A comprehensive first aid and healthcare mobile app built with Flutter. Features include emergency contact management, first aid instructions, illness database, hospital locator, training modules, health journaling, symptom analysis, and community support. Uses Firebase for backend services and includes offline capabilities.

AI Swasthya Sathi is an intelligent healthcare assistant that combines computer vision and large language models to provide real-time symptom analysis, AI-driven guidance, and secure patient data management, offering an interactive and practical solution to make healthcare more accessible and smarter.

🏥 AI-powered medical report generation with RAG, HIPAA compliance, and Next.js frontend

AI-driven disease prediction model based on symptoms using machine learning.

VAERS Adverse Event Analysis for COVID 19 Vaccine : A hybrid approach combining LLMs (Gemini 1.5 Flash) and statistical methods for enhanced vaccine safety signal detection. Analyzes temporal and associative relationships in VAERS symptom data.

SVM-based COVID-19 prediction model with cross-validation and full performance evaluation (precision, recall, F1-score, confusion matrix).

To investigate brain network dynamics linked with dimensionally-based symptom profiles exhibited across a transdiagnostic cohort of participants with and without psychiatric diagnoses.

Research pipeline testing the STRAIN hypothesis: Do TikTok creators reporting EDS, MCAS, POTS, and CIRS actually share a common stress-activated neurogenic phenotype? AI-powered symptom extraction and narrative analysis from social media.

IEEE-published preventive healthcare symptom analysis PWA with offline fallback and Gemini proxy

Machine learning–based disease prediction system that identifies the most likely illness from input symptoms and provides relevant medication suggestions. Built with Python, scikit-learn, and Flask as part of a graduate research project at Montclair State University

Context-Aware AI Symptom Analyzer is an intelligent healthcare system that analyzes user-reported symptoms using AI and contextual data to generate more accurate, personalized health insights. Unlike traditional symptom checkers, it considers user context such as history, patterns, and conditions to improve relevance and decision support.