[ToC] Course 3

ToC of Course 3/5: Fundamentals of Machine Learning for Healthcare #

Module 3: Concepts and Principles of Machine Learning in Healthcare #

  1. Introduction to Deep Learning and Neural Networks
  2. Deep Learning and Neural Networks
  3. Cross Entropy Loss
  4. Gradient Descent
  5. Representing Unstructured Image and Text Data
  6. Convolutional Neural Networks
  7. Natural Language Processing and Recurrent Neural Networks
  8. The Transformer Architecture for Sequences
  9. Commonly Used and Advanced Neural Network Architectures
  10. Advanced Computer Vision Tasks and Wrap-Up

Module 4: Evaluation and Metrics for Machine Learning in Healthcare #

  1. Introduction to Model Performance Evaluation
  2. Overfitting and Underfitting
  3. Strategies to Address Overfitting, Underfitting and Introduction to Regularization
  4. Statistical Approaches to Model Evaluation
  5. Receiver Operator and Precision Recall Curves as Evaluation Metrics

Module 5: Strategies and Challenges in Machine Learning in Healthcare #

  1. Introduction to Common Clinical Machine Learning Challenges
  2. Utility of Causative Model Predictions
  3. Context in Clinical Machine Learning
  4. Intrinsic Interpretability
  5. Medical Data Challenges in Machine Learning Part 1
  6. Medical Data Challenges in Machine Learning Part 2
  7. How Much Data Do We Need?
  8. Retrospective Data in Medicine and “Shelf Life” for Data
  9. Medical Data: Quality vs Quantity

Module 6: Best Practices, Teams, and Launching Your Machine Learning Journey #

  1. Clinical Utility and Output Action Pairing
  2. Taking Action - Utilizing the OAP Framework
  3. Building Multidisciplinary Teams for Clinical Machine Learning
  4. Governance, Ethics, and Best Practices
  5. On Being Human in the Era of Clinical Machine Learning
  6. Death by GPS and Other Lessons of Automation Bias

Module 7: Foundation Models #

  1. Introduction to Foundation Models
  2. Adapting to Technology
  3. General AI and Emergent Behavior
  4. How Foundation Models Work
  5. Healthcare Use Cases for Text Data
  6. Healthcare Use Cases for Non-textual Unstructured Data
  7. Challenges and Pitfalls
  8. Conclusion