In recent years, reinforcement learning (RL) has acquired a prominent position in the space of health-related sequential decision-making, becoming an increasingly popular tool for delivering adaptive interventions (AIs). However, despite potential …
Our aim is to provide a multi-disciplinary assessment of how fairness for machine learning fits into the context of clinical trials research and practice. We start by reviewing the current ethical considerations and guidelines for clinical trials and examine their relationship with common definitions of fairness in machine learning. We examine potential sources of unfairness in clinical trials, providing concrete examples, and discuss the role machine learning might play in either mitigating potential biases or exacerbating them when applied without care.
The study of evidence-based dynamic treatment regimes (DTRs) comprises an important line of methodological research within the domain of personalized medicine, a medical paradigm that transitions from the one-size-fits-all ideology. In this chapter …
Our aim is to provide a multi-disciplinary assessment of how fairness for machine learning fits into the context of clinical trials research and practice. We start by reviewing the current ethical considerations and guidelines for clinical trials and examine their relationship with common definitions of fairness in machine learning. We examine potential sources of unfairness in clinical trials, providing concrete examples, and discuss the role machine learning might play in either mitigating potential biases or exacerbating them when applied without care.
Using bandit algorithms to conduct adaptive randomised experiments can minimise regret, but it poses major challenges for statistical inference (e.g., biased estimators, inflated type-I error and reduced power). Recent attempts to address these …
Low physical activity is an important risk factor for common physical and mental disorders. Physical activity interventions delivered via smartphones can help users maintain and increase physical activity, but outcomes have been mixed. Here we …
Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative interventions to help students learn) are assigned to participants, with …
Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine …