AI for Multimodal Diseases Prediction
Integrating multi-omics, imaging, and clinical data to predict disease progressio
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AI for Multimodal Disease Prediction
Integrating multi-omics, imaging, and clinical data to predict disease progression
This project aims to develop artificial intelligence methods to predict the progression of chronic respiratory diseases, including sarcoidosis, COPD, asthma, and interstitial lung disease (ILD). Current clinical tools are limited in their ability to capture disease heterogeneity and accurately forecast individual patient trajectories.
Our work integrates multi-omics data with chest imaging, pulmonary function tests, and electronic health records to build predictive models of disease progression. By applying advanced machine learning approaches, we identify patterns across diverse data types that reflect underlying disease biology.
Ultimately, this project seeks to enable accurate, personalized prediction of disease outcomes and support precision medicine approaches for chronic lung disease.
