Bandit Algorithms for Precision Medicine

by   Yangyi Lu, et al.

The Oxford English Dictionary defines precision medicine as "medical care designed to optimize efficiency or therapeutic benefit for particular groups of patients, especially by using genetic or molecular profiling." It is not an entirely new idea: physicians from ancient times have recognized that medical treatment needs to consider individual variations in patient characteristics. However, the modern precision medicine movement has been enabled by a confluence of events: scientific advances in fields such as genetics and pharmacology, technological advances in mobile devices and wearable sensors, and methodological advances in computing and data sciences. This chapter is about bandit algorithms: an area of data science of special relevance to precision medicine. With their roots in the seminal work of Bellman, Robbins, Lai and others, bandit algorithms have come to occupy a central place in modern data science ( Lattimore and Szepesvari, 2020). Bandit algorithms can be used in any situation where treatment decisions need to be made to optimize some health outcome. Since precision medicine focuses on the use of patient characteristics to guide treatment, contextual bandit algorithms are especially useful since they are designed to take such information into account. The role of bandit algorithms in areas of precision medicine such as mobile health and digital phenotyping has been reviewed before (Tewari and Murphy, 2017; Rabbi et al., 2019). Since these reviews were published, bandit algorithms have continued to find uses in mobile health and several new topics have emerged in the research on bandit algorithms. This chapter is written for quantitative researchers in fields such as statistics, machine learning, and operations research who might be interested in knowing more about the algorithmic and mathematical details of bandit algorithms that have been used in mobile health.


page 1

page 2

page 3

page 4


Machine Learning in Precision Medicine to Preserve Privacy via Encryption

Precision medicine is an emerging approach for disease treatment and pre...

Machine learning and genomics: precision medicine vs. patient privacy

Machine learning can have major societal impact in computational biology...

A Biologically Plausible Benchmark for Contextual Bandit Algorithms in Precision Oncology Using in vitro Data

Precision oncology, the genetic sequencing of tumors to identify druggab...

Measurement error and precision medicine: error-prone tailoring covariates in dynamic treatment regimes

Precision medicine incorporates patient-level covariates to tailor treat...

The Risk to Population Health Equity Posed by Automated Decision Systems: A Narrative Review

Artificial intelligence is already ubiquitous, and is increasingly being...

Optimising Individual-Treatment-Effect Using Bandits

Applying causal inference models in areas such as economics, healthcare ...

An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions

Increasing technological sophistication and widespread use of smartphone...

Please sign up or login with your details

Forgot password? Click here to reset