46 citations · 81 across the 9 of their papers we have counts for
9 papers
Learning medical triage from clinicians using Deep Q-Learning
Albert Buchard, Baptiste Bouvier, Giulia Prando +10
Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately…
Masking schemes for universal marginalisers
Divya Gautam, Maria Lomeli, Kostis Gourgoulias +2
We consider the effect of structure-agnostic and structure-dependent masking schemes when training a universal marginaliser (arXiv:1711.00695) in order to learn conditional distrib…
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Robert Walecki, Kostis Gourgoulias, Adam Baker +7
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference…
Leveraging directed causal discovery to detect latent common causes
Ciarán M. Lee, Christopher Hart, Jonathan G. Richens +1
The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two var…
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
Yura Perov, Logan Graham, Kostis Gourgoulias +4
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programmin…
Counterfactual diagnosis
Jonathan G. Richens, Ciaran M. Lee, Saurabh Johri
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases \e…
Universal Marginalizer for Amortised Inference and Embedding of Generative Models
Robert Walecki, Albert Buchard, Kostis Gourgoulias +6
Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable nu…
A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis
Salman Razzaki, Adam Baker, Yura Perov +10
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence…
A Universal Marginalizer for Amortized Inference in Generative Models
Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias +6
We consider the problem of inference in a causal generative model where the set of available observations differs between data instances. We show how combining samples drawn from t…