📘 Course 3: Fundamentals of ML for Healthcare #

🤖 Module 1: Why Machine Learning in Healthcare? #

1. What’s the Problem?
Understanding how ML fits into the healthcare ecosystem and why traditional models are insufficient.

2. Why Does It Matter?
ML has the potential to improve diagnosis, patient care, and reduce costs, but it also raises ethical and technical concerns.

3. What’s the Core Idea?
Machine learning leverages data to improve healthcare predictions, enabled by access to digital health data and modern computing.

4. How Does It Work?
ML systems learn mappings between inputs and outputs from data, using statistical methods rather than hard-coded rules.

5. What’s Next?
Explore the basic types and terminology of ML, including supervised and unsupervised learning.

📘 Module 2: Concepts and Principles of ML in Healthcare Part 1 #

1. What’s the Problem?
Clarifying foundational ML terms and how ML differs from traditional programming.

2. Why Does It Matter?
Foundational understanding is necessary for applying ML models correctly in clinical settings.

3. What’s the Core Idea?
ML learns functions that map inputs to outputs using labeled (or unlabeled) data. Supervised learning uses labels; unsupervised does not.

4. How Does It Work?
ML involves preprocessing data, training models, validating predictions, and testing accuracy with structured data splits.

5. What’s Next?
Delve into deep learning, neural network architectures, and their applications in healthcare.

🧠 Module 3: Concepts and Principles of ML in Healthcare Part 2 #

1. What’s the Problem?
Applying and interpreting complex ML models like deep neural networks in healthcare.

2. Why Does It Matter?
Neural networks enable breakthroughs in imaging and text analysis but require understanding to avoid misuse.

3. What’s the Core Idea?
Deep learning uses layers of neurons to learn complex mappings. CNNs are suited for images; RNNs and Transformers for sequences.

4. How Does It Work?
Training uses backpropagation and loss optimization. Model performance is evaluated with metrics like AUROC, accuracy, and precision.

5. What’s Next?
Evaluate ML models using statistical metrics, assess overfitting, and explore model generalizability.

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

1. What’s the Problem?
Ensuring ML models are reliable and generalizable before clinical deployment.

2. Why Does It Matter?
Poorly evaluated models may be unsafe or ineffective in high-stakes healthcare settings.

3. What’s the Core Idea?
Evaluation includes accuracy, AUROC, precision-recall, and more. Proper data splits and validation strategies are critical.

4. How Does It Work?
Use learning curves, loss plots, cross-validation, and hyperparameter tuning to evaluate models. Choose appropriate thresholds.

5. What’s Next?
Understand practical barriers and strategies for deploying ML in real clinical environments.

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

1. What’s the Problem?
Dealing with real-world limitations like data bias, label noise, interpretability, and clinical relevance.

2. Why Does It Matter?
Models that lack robustness or interpretability may fail in practice or perpetuate health disparities.

3. What’s the Core Idea?
Tactics include regularization, domain-specific feature engineering, human-centered design, and sensitivity to healthcare context.

4. How Does It Work?
Apply dropout, saliency mapping, ensemble learning, and transparent reporting. Collaborate with clinicians for contextual validation.

5. What’s Next?
Build multidisciplinary teams and prepare for real-world deployment including ethical review and monitoring.

🚀 Module 6: Best Practices, Teams, and Launching Your ML Journey #

1. What’s the Problem?
Bridging the gap between ML research and real-world clinical implementation.

2. Why Does It Matter?
Success depends on team composition, ethics, data stewardship, and designing for human-AI interaction.

3. What’s the Core Idea?
Use frameworks like Output-Action Pairing to define measurable goals. Involve stakeholders throughout the development cycle.

4. How Does It Work?
Form teams with technical, clinical, ethical, and operational expertise. Start small, iterate, and evaluate continuously.

5. What’s Next?
Identify your project focus, assemble collaborators, and begin experimenting responsibly.