Electronic health records and high throughput measurement technologies are changing the practice of healthcare to become more algorithmic and data-driven. This offers an exciting opportunity for machine learning to impact healthcare. A key challenge, however, is the heterogeneity of disease expression across people; a model that works well for one patient may perform very poorly for another. One solution is to build personalized models that blend information from a population and from the current individual to provide tailored inferences.
This tutorial will discuss ideas from machine learning that enable personalization (useful for applications in education, retail, medicine and recommender systems more broadly). The tutorial will focus on applications in healthcare and medicine. We will cover:
- Bayesian hierarchical models
- Transfer learning and multi-resolution sharing
- Functional data analysis
- Causal inference and individualized treatment effects
- Potential outcomes
- Strategies for adjusting for confounding
- Sequential and time-varying treatments
- Bayesian estimation of individualized treatment response
- "Causal Risk" and What-if Reasoning
- Dynamic treatment regimes
- Estimating optimal treatment rules
- Connections to reinforcement learning
Ultimately, the goal is to build individual-specific decision support tools that enable a data-driven understanding of alternative interventions by answering "what if?" questions: e.g. what would happen if I gave this patient drug A vs. drug B?
Target audience: The majority of this tutorial will be targeted at an audience with basic machine learning knowledge. No background in medicine or health care is needed.
Learning objectives: - Become familiar with important computational problems in precision medicine and individualized health care, understand key ideas behind personalized machine learning, and become familiar with state-of-the-art techniques used to build personalized decision-making tools.