Asthma Mechanisms and AI Integration
Integrated platforms for AI-assisted asthma prediction and asthma mechanistic studies
Asthma is a chronic and widespread respiratory condition that affects millions of individuals globally, posing significant public health and economic challenges. Despite advances in asthma research and the development of predictive models, there remains a lack of accessible, comprehensive platforms offering real-time, personalized risk assessments and management tools for both the public and healthcare providers. This project aims to address that gap by developing an integrated, AI-powered platform for precise asthma prediction, data management, and mechanistic research. The platform features user-friendly prediction tools and centralized data collection capabilities for clinicians, facilitating collaboration and accelerating scientific discovery in asthma-related studies. The project’s beta version of the web tool is now available for public use and can be accessed at: https://memphisiow.shinyapps.io/AsthmaPrediction/. Mobile users can conveniently scan the provided QR code to access the tool.
The platform integrates multiple predictive models, including the four ASPIRE models, for asthma risk assessments. For the public, it offers AI-driven asthma predictions along with personalized recommendations, empowering individuals to make informed health decisions and adopt proactive management strategies. For healthcare providers, the platform is designed to streamline patient monitoring and data collection, enhancing clinical efficiency and improving patient outcomes. It also ensures secure data handling, adhering to privacy regulations such as HIPAA.
The potential impact of this project is far-reaching. The project aims to significantly enhance asthma prediction, prevention, and care. It will foster innovation in respiratory health research and contribute to the broader effort of reducing the burden of asthma on individuals and healthcare systems. The joint work is lead by a productive team which include Drs. Yu (Joyce) Jiang, Hongmei Zhang, Meredith Ray, Qianyi Cheng, Yongmei Wang, and Xiajun Jiang.
