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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
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)
Organiser contact email address:
events@stats.ox.ac.uk
Hosts:
Professor Christl Donnelly (University of Oxford),
Professor Simon Myers (University of Oxford)
Part of:
Florence Nightingale Annual Lecture
Booking required?:
Required
Booking url:
https://www.stats.ox.ac.uk/events/florence-nightingale-lecture-2024
Cost:
Free of charge
Audience:
Members of the University only
Editor:
Beverley Lane
