My research integrates data science and geriatric medicine to explore what different data sources and methods can tell us about frailty, a state of increased vulnerability to adverse health outcomes for individuals of the same chronological age.
Speaker: Lara Johnson
In this talk, I will discuss work on building ML model that takes into account prior knowledge of common activity patterns for the activity recognition task and uses the same model to forecast the individualized activity routine.
Speaker: Simon U
We describe our work on developing a GUI for the PROTER simulator that supports its existing core functionality, which is currently only available programmatically.
Speaker: Gareth Dawson
Activities of Daily Living (ADLs) behaviour patterns and daily routines can give many insights into a person's mental and physical state. This work aims to detecting long-term deviation in ADLs based on sensor data.
Speaker: Leo Kravtchin
Co-morbidity networks (a.k.a. Phenotypic disease networks) capture associations between morbidities that can be later used to explore disease progression, find typical co-morbidity patterns, etc. We describe a new Bayesian approach that infers the latent target quantities from the data while also providing full statistical distributions of the inferred quantities and other network metrics.
Speaker: Guillermo Romero Moreno
In this talk, I will describe the network-based Agent-based model we developed to reproduce the social contagion process of tobacco use. Our results suggest that network structure is essential and that the observed dynamics from the ODE model are only similar to the network-based ABM only when the underlying network is fully connected, which is rarely the case.
Speaker: Adarsh Prabhakaran
We describe our plans to explore how patient health data can affect and inform clinical pathways, especially at the early stage of hospital admission. We aim to predict the potential pathway that a new intensive care patient may take, focusing on possible deviations as they may indicate complications in their conditions and associated treatments.
Speaker: Zonglin Ji
In this talk, I will present four different brain network models derived from magnetic resonance imaging and discuss applications where features derived from brain networks are used to train predictive models and characterise clinical populations
Speaker: Paola Galdi
In this talk, I will describe my plans for work in neurosymbolic AI for biomedical applications that demonstrate ways to utilize the unique abilities of its methods for common challenges surrounding biomedical data.
Sepaker: Lauren DeLong
I will talk about my work on formalising Algebraic QFT (AQFT) in Isabelle/HOL.
Speaker: Richard Schmoetten