UAI 2017 Tutorial: Suchi Saria & Hossein Soleimani
Machine Learning and Counterfactual Reasoning for "Personalized" Decision-Making in Healthcare
Suchi Saria and Hossein Soleimani, Johns Hopkins University
Slides
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 statistical machine learning techniques to impact healthcare. The aim of this tutorial is to introduce you to challenges and techniques for developing “personalized decision-making” tools in medicine. Example topics covered will include: scalable joint models for forecasting risk from "messy" clinical streams, estimating individualized treatment response in populations with heterogeneous treatment effects, and examples of counterfactual reasoning or "what-if" analysis for decision-making. We will also cover example data sources and describe ongoing national initiatives that provide a way for you to get involved. 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. We will make our slides and any relevant documents accessible after the talk.
Machine Learning and Counterfactual Reasoning for “Personalized” Decision-Making in Healthcare
Suchi Saria and Hossein Soleimani, Johns Hopkins University
Slides
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 statistical machine learning techniques to impact healthcare. The aim of this tutorial is to introduce you to challenges and techniques for developing “personalized decision-making” tools in medicine. Example topics covered will include: scalable joint models for forecasting risk from “messy” clinical streams, estimating individualized treatment response in populations with heterogeneous treatment effects, and examples of counterfactual reasoning or “what-if” analysis for decision-making. We will also cover example data sources and describe ongoing national initiatives that provide a way for you to get involved. 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. We will make our slides and any relevant documents accessible after the talk.
