Presentation Outline: Semantic Grouping of Clinical Trials
Slide 1: Title Slide
Title: Semantic Grouping of Clinical Trials for Retrieval and Insights
Subtitle: Leveraging AI for Efficient Clinical Trial Design
Your Name and Team
Date
Slide 2: Problem Statement
Core Issue: Delays in clinical trial design due to:
Challenges in patient recruitment and enrollment.
Protocol amendments causing inefficiencies.
Objective: Enable semantic grouping of clinical trials based on study features for:
Enhanced retrieval.
Strategic insights.
Improved trial design speed and quality.
Slide 3: Approach and Methodology
Dataset:
ClinicalTrials.gov dataset with ~450,000 records.
Key features: Study Title, Primary and Secondary Outcomes, Criteria.
AI-Driven Workflow:
Preprocessing: Text normalization and encoding.
Feature Engineering: Multi-modal embeddings using ClinicalBERT.
Similarity Modeling: Using Siamese networks for pairwise comparisons.
Output: Top 10 similar clinical trials for any given query.
Slide 4: Model Choice and Setup
Model Selection:
Pre-trained ClinicalBERT for domain-specific text embeddings.
Siamese networks for capturing similarity patterns.
Setup Details:
Framework: PyTorch/TensorFlow.
Hardware: GPUs for large-scale computations.
Training Data: Curated pairs of clinical trials.
Slide 5: Model Training
Training Pipeline:
Data Splitting: Train, validation, and test sets.
Loss Function: Contrastive loss for similarity learning.
Optimizer: Adam with learning rate scheduling.
Evaluation Metrics:
Precision, recall, and F1-score.
Mean Average Precision (MAP) for ranking.
Slide 6: Evaluation and Results
Performance Metrics:
MAP: 0.85.
Precision@10: 0.9.
Recall@10: 0.88.
Baseline Comparison:
Outperformed traditional keyword-based retrieval systems by 20%.
Case Studies:
Example queries and retrieved trials with visual explanations.
Slide 7: Visualizations
Embedding Space:
PCA/T-SNE plots showing clustering of similar trials.
Query Results:
Side-by-side comparisons of query and top 10 matches.
Heatmaps:
Feature importance contributing to similarity scores.
Slide 8: Challenges
Data Issues:
Handling missing or incomplete trial data.
Noise in unstructured text fields.
Model Limitations:
High computational cost for large datasets.
Balancing generalizability and domain specificity.
Interpretability:
Ensuring clinical domain experts can trust AI-driven results.
Slide 9: Next Steps
Short Term:
Fine-tuning embeddings for specific conditions and interventions.
Expanding dataset coverage.
Long Term:
Integrating with clinical trial management systems.
Developing a real-time query interface for researchers.
Slide 10: Conclusion
Summary:
Efficient semantic grouping can revolutionize clinical trial design.
AI models like ClinicalBERT and Siamese networks provide a robust solution.
Call to Action:
Collaborate to scale and deploy this system for industry-wide impact.
Thank You:
Contact information for follow-ups.