Preview Python and ML in Clinical Trials: Comprehensive Analytics for Advanced Monitoring

Welcome to the Course

Week1: Introduction to Clinical Trials and Data Science

Lecture 1: Welcome to Clinical Trials in the Digital Age

Lecture 2: Overview of Clinical Trial Phases

Lecture 3: Introduction to Data Science in Clinical Trial

Week 1 Quiz: Introduction to Clinical Trials and Data Science

Week 2: Data Handling and Preprocessing

Lecture 6: "Introduction to Python for Data Science"

Lecture 4: "Data Collection in Clinical Trials"

Lecture 5: "Data Cleaning and Preprocessing Techniques"

Week3: Deep Dive into Monitoring

Lecture 7: "Foundations of Monitoring in Clinical Trials"

Lecture 8: "Metrics and Measures: Monitoring Clinical Data"

Assignment 2: Analyze a dataset to identify monitoring metrics and propose a monitoring plan.

Week 4: Monitoring with Python and ML

Lecture 9: Automating Data Monitoring with Python

Lecture 10: Real-time Monitoring and Alerts with ML

Week 5: Advanced Monitoring Techniques

Lecture 11: "Predictive Analytics in Monitoring"

Lecture 12: "Machine Learning for Predictive Monitoring"

Assignment 3: Build a predictive model for risk or deviation detection in a clinical trial dataset.

Week 6: Interpretability and Transparency in Monitoring

Lecture 13: "Ensuring Transparency in ML-based Monitoring"

Lecture 14: "Interpretable ML Models for Monitoring"

Week 7: Synthesizing Monitoring Insights

Lecture 15: "From Monitoring Data to Insights"

Lecture 16: "Actionable Reporting: Communicating Monitoring Insights"

Assignment 4: Analyze a comprehensive monitoring dataset and prepare a report detailing findings and recommendations.

Week 8: Ethics, Future, and Conclusion

Lecture 17: "Challenges and Ethical Considerations in Data-Driven Clinical Trials"

Lecture 18: "The Future of Clinical Trials: Trends and Innovations"

Final Course Assessment