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Gain hands-on experience with machine learning for healthcare data analysis

Healthcare data are often available to payers and healthcare systems in real time, but are massive, high dimensional, and complex. Artificial intelligence and machine learning merge statistics, computer science, and information theory and offer powerful computational tools to enhance the extraction of useful information from complex healthcare data and prediction accuracy. This course gives an overview of basic machine learning concepts and introduces a few commonly used machine learning techniques and their practical applications in healthcare and pharmaceutical outcomes research.

Technical topics include:

• Foundational principles and concepts of statistical machine learning.

• Commonly used machine learning techniques: penalized regression, tree-based methods, and dimension reduction/feature selection.

• Practical applications of machine learning in health and pharmaceutical outcomes research.

• Techniques for interpreting and evaluating results and prediction performance from machine learning models.

• Distinguishing prediction modeling from causal inference research in pharmacoepidemiology.

This course includes hands-on learning and tools that can be immediately applied, including:

• Practical exercises using R or Radiant to demonstrate machine learning methods.

• Hands-on experiences with penalized regression, tree-based methods, and dimension reduction.

• Guided sessions on interpreting and evaluating machine learning results.

• Real-world case studies to explore the applications of machine learning in healthcare.

Participants will leave the course with practical skills and a deeper understanding of how to apply machine learning techniques to complex healthcare data.

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This is an entry-level course but is designed for those with some familiarity with traditional statistical modeling techniques (eg, linear regression, logistic regression).

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