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

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

📅 Day 1: Clinical-Omics Multimodal AI and COMET Research Foundations

• Global trends in clinical AI, omics integration, and precision medicine
• Overview of clinical data, omics data, biomarkers, and EHR records
• Introduction to the COMET Framework and research-replication approach
• Data scarcity challenges in biomedical and omics studies
• Multimodal fusion strategies: early fusion, late fusion, and hybrid fusion
• Research use cases: disease prediction, patient stratification, and biomarker discovery

🛠️ Hands-on:

• Hands-on Activity: Create a synthetic clinical + omics + EHR-style dataset in Google Colab and prepare it for multimodal analysis.

🧰 Tools Covered: Google Colab

📅 Day 2: Transfer Learning, Foundation Models, and Transformer-Based Fusion

• Transfer learning for small biomedical datasets
• Biomedical foundation models and pretrained clinical representations
• Transformer models for EHR text and clinical notes
• Feature engineering for clinical and omics data
• Combining tabular features with transformer-based text embeddings
• Handling missing modalities and small-cohort limitations
• Building a COMET-inspired multimodal prediction pipeline

🛠️ Hands-on:

• Hands-on Activity: Build a multimodal disease-risk prediction model using clinical features, omics features, and EHR-style text embeddings in Google Colab.

🧰 Tools Covered: Google Colab

📅 Day 3: Benchmarking, Explainability, Responsible AI, and Research Replication

• Comparing unimodal, bimodal, and multimodal model performance
• Model evaluation using accuracy, F1-score, ROC-AUC, and confusion matrix
• Benchmarking and reproducibility in biomedical AI research
• Explainability for clinical and omics predictions using feature importance
• Responsible AI: privacy, bias, fairness, and clinical trust
• Translational applications in precision medicine, pharma, and healthcare analytics
• Preparing a mini research-style replication summary

🛠️ Hands-on:

• Hands-on Activity: Evaluate the COMET-inspired model, interpret key features, visualize results, and prepare a mini research-style report in Google Colab.

🧰 Tools Covered: Google Colab

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

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