Machine Learning for Mobile Health

Course Description

Welcome to Machine Learning for Mobile Health, an advanced course focusing on the integration of machine learning techniques and technologies with mobile applications for the advancement of healthcare solutions. This comprehensive program covers critical topics such as Mobile Crowd Sensing, Digital Health Apps, Mobile Application Engineering, Machine Learning Pipelines using CRISP-DM, Statistics and Populations, Data Preparation, Feature Engineering, Tree-based Machine Learning Models, Machine Learning Metrics, Cross-Validation, Artificial Intelligence (AI) Explainability, AI Ethics, Regulations, and Statistical Method: Linear Component Analysis.

Course Outline

1. Introduction to Digital Health and Mobile Applications

   • Overview of digital health technologies
   • The role and importance of mobile applications in healthcare
   • Ethical considerations and regulatory frameworks for digital health apps

2. Mobile Crowd Sensing and Digital Health Apps

   • Understanding Mobile Crowd Sensing and its applications in healthcare
   • Designing and implementing digital health applications

3. Mobile Application Engineering

   • Principles and techniques for mobile application development
   • UI/UX design considerations for health-focused applications
   • Security and privacy in mobile app engineering

4. Machine Learning Pipelines with CRISP-DM

   • Overview and implementation of the CRISP-DM framework
   • Data collection, cleaning, and preprocessing
   • Model selection, training, and evaluation in a pipeline

5. Statistics and Populations in Healthcare

   • Understanding statistical methods relevant to healthcare
   • Analysis of populations and healthcare data
   • Application of statistical tests for healthcare research

6. Data Preparation and Feature Engineering

   • Techniques for data preparation for machine learning
   • Feature engineering for enhancing model performance
   • Handling imbalanced data and missing values

7. Tree-based Machine Learning Models

   • In-depth study of tree-based machine learning algorithms (e.g., Decision Trees, Random Forest, XGBoost)
   • Optimization and hyperparameter tuning for tree-based models

8. Machine Learning Metrics and Cross-Validation

   • Understanding and using metrics for model evaluation (classification and regression)
   • Cross-validation techniques for robust model assessment

9. Artificial Intelligence: Explainability and Ethics

   • Techniques for explaining AI models and decisions
   • Ethical considerations and responsible AI practices in healthcare
   • Compliance with regulations in AI for healthcare

10. Statistical Method: Linear Component Analysis

    • Introduction to Linear Component Analysis (LCA)
    • Understanding its applications in healthcare data analysis
    • Implementing LCA for healthcare research

This course will equip participants with the skills and knowledge to effectively leverage machine learning techniques in mobile health applications, contributing to the enhancement of healthcare services and outcomes. Join us in this exciting journey at the intersection of machine learning and mobile health!