Statistics, nursing, and social reform: Following in the footsteps of Florence Nightingale
Decision-making tasks in healthcare settings use methods that make a number of assumptions that we know are violated in clinical data. For example, clinicians do not always act optimally; clinicians are more or less aggressive in treating patients; clinicians have biases; and patients have (often unobserved) conditions that lead to differential response to interventions. In this talk, and following in Florence Nightingale’s path, I will walk through a handful of these violated assumptions and discuss statistical reinforcement learning and inverse reinforcement learning methods to address these violated assumptions. I will show on a number of scenarios, including sepsis treatment and electrolyte repletion, that these methods that have more flexible assumptions than existing methods lead to substantial improvements in decision-making tasks in clinical settings, reducing bias and leading to improved clinical outcomes.
Date: 23 February 2024, 15:30 (Friday, 6th week, Hilary 2024)
Venue: 24-29 St Giles', 24-29 St Giles' OX1 3LB
Venue Details: Large Lecture Theatre, Department of Statistics
Speaker: Professor Barbara Engelhardt (Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science)
Organising department: Department of Statistics
Organiser: Beverley Lane (Department of Statistics, University of Oxford)
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Hosts: Professor Christl Donnelly (University of Oxford), Professor Simon Myers (University of Oxford)
Part of: Florence Nightingale Annual Lecture
Booking required?: Required
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Cost: Free of charge
Audience: Members of the University only
Editor: Beverley Lane