Clinical & Omics AI: Multimodal EHR-Genomics Analysis Using Transfer Learning

This 3-day hands-on workshop introduces participants to the COMET Framework, a research-inspired approach for multimodal biomedical AI. The workshop focuses on integrating clinical data, omics features, biomarkers, and EHR-style text using transfer learning, transformer-based representations, and multimodal fusion techniques. Designed for researchers, academicians, and industry professionals, this workshop helps participants understand how modern AI can support data-scarce biomedical research, disease-risk prediction, precision medicine, patient stratification, and biomarker discovery. Participants will work through Google Colab-based hands-on activities to build a simplified COMET-inspired clinical-omics AI pipeline.

The aim of this workshop is to provide participants with practical and research-oriented knowledge of clinical-omics multimodal AI, transfer learning, and transformer-based data fusion for biomedical research.

The workshop aims to help participants understand how clinical records, omics data, and EHR-style text can be combined to develop AI models that support biomedical prediction, research replication, and precision medicine applications.

By attending this workshop, participants will learn to:

• Understand the role of multimodal AI in biomedical and clinical research
• Explore the COMET Framework for clinical, omics, and EHR data integration
• Understand data scarcity challenges in biomedical AI research
• Learn transfer learning concepts for small biomedical datasets
• Understand transformer-based representations for EHR-style text
• Explore early fusion, late fusion, and hybrid fusion strategies
• Build a simplified clinical-omics multimodal AI pipeline in Google Colab
• Compare unimodal and multimodal model performance
• Apply basic model evaluation and explainability techniques
• Connect the workflow with research replication, precision medicine, and biomarker discovery

After completing this workshop, participants will be able to:

• Explain how clinical data, omics features, biomarkers, and EHR-style text can be integrated using multimodal AI
• Understand the importance of transfer learning in data-scarce biomedical research
• Prepare a basic clinical + omics + EHR-style dataset for AI modeling
• Build a simplified COMET-inspired multimodal prediction pipeline
• Use transformer-based embeddings for unstructured clinical text representation
• Evaluate model performance using accuracy, F1-score, ROC-AUC, and confusion matrix
• Interpret important clinical and omics features from model outputs
• Understand responsible AI considerations such as privacy, bias, fairness, and clinical trust
• Develop a mini research-style summary from model results and visualizations
• Apply the learning to biomedical research areas such as disease prediction, patient stratification, biomarker discovery, precision medicine, and healthcare analytics

This workshop is suitable for:

• Researchers working in biomedical science, clinical AI, bioinformatics, omics, or healthcare analytics
• Academicians and faculty members interested in AI-driven biomedical research
• PhD scholars and postgraduate learners working with healthcare, genomics, or biomedical datasets
• Industry professionals from biotechnology, pharma, healthcare AI, diagnostics, and precision medicine
• Data science and AI professionals interested in clinical and biomedical applications
• Professionals who want to understand multimodal AI, transfer learning, and EHR-based biomedical modeling