What do data tell us about frailty?

Date: 26th January 2023
Time: 13:30 - 15:00

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

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Bayesian inference of disease co-morbidity networks from electronic health records

Date: 21st October 2022
Time: 14:30 - 15:00

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

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Networks for smoking dynamics

Date: 21st October 2022
Time: 14:00 - 14:30

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

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Understanding the Clinical Pathways of Intensive Care Patients using AI

Date: 20th October 2022
Time: 16:00

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

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