Henrique Aguiar

Henrique pursued undergraduate studies at the University of Oxford, where I completed my Master in Mathematics from Balliol College in 2019. His Master’s thesis was on Deep Learning and studying how differential equations could be used to construct new Neural Network architectures. His interests span a wide range of areas in theoretical Statistics and Data Science; in particular, its applications to healthcare. This includes topics such as Image Analysis, High-dimensional Statistics and Bayesian Inference.

Throughout his undergraduate degree, Henrique had the chance to undertake internships in wealth management and Deep Learning for Image Recognition, as well as research projects in Number Theory, and both Topological Data Analysis and Statistical Machine Learning for Healthcare.

Henrique's DPhil focuses on the development of clustering methodologies on multivariate, multi-modal patient vital-sign observations and Electronic Health Records (EHR) information (including laboratory test results, medications, blood tests, cohort and demographic data)
that are capable of predicting patient trajectories, and which, ultimately, deliver personal monitoring recommendations to boost patient well-being.

Henrique's work aims to develop methods focused on risk and trajectory prediction
for hospital patients, through deriving patient subtypes and representative trajectories. He will seek to extend these to capture and incorporate other event types, and recommend optimal interventions. His current work is focused on COPD, and he ultimately aims to generalise a model that will be applicable to other diseases such as cardiovascular disease (CVD).

I am currently working under the supervision of Dr Tingting Zhu on the use of vital-sign features, as well as cohort and outcome information, from hospital databases and public datasets, to improve prediction of patient trajectories and deliver personal monitoring recommendations to boost patient well-being.

H. Aguiar, M. Santos, P. Watkinson, T. Zhu (2020) Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease,