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Short Course* Registration: 7:30AM-12:00PM

This short course will provide an overview of statistical machine learning and artificial intelligence techniques with applications to precision medicine, in particular to deriving optimal individualized treatment strategies for precision medicine. This course will cover both treatment selection and treatment transition. The treatment selection framework is based on outcome-weighted classification. We will cover logistic regression, support vector machine (SVM)-learning, robust SVM, and angle-based classifiers for multi-category learning, and we will show how to modify these classification methods into outcome-weighted learning algorithms for precision medicine. The second part of this course will cover treatment transition. We will provide an introduction on reinforcement learning techniques. Algorithms, including dynamic programming for Markov Decision Process, temporal difference learning, SARSA, Q-Learning algorithms, and actor-critic methods, will be covered. We will discuss on how to use these methods for developing optimal treatment transition strategies. The techniques discussed will be demonstrated in R.
*Short Courses are not included in the conference registration and require a separate fee.

Return to Biostatistics Industry and Regulator Forum.

Learning objectives

At the conclusion of this short course, participants should be able to:

  • Understand the connections between modern machine learning/AI algorithms and precision medicine
  • Apply statistical machine learning techniques to address problems in personalized medicine and other biomedical applications.
  • Identify problems which can be solved by reinforcement learning algorithms

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