Research Fellow, Wolfson College, Oxford
Lecturer, Lady Margaret Hall, Oxford
Institute of Biomedical Engineering
University of Oxford
Old Road Campus Research Building
Oxford OX3 7DQ, UK

Research interest:

Data Analytics: Machine Learning, Statistics, Signal Processing, Modelling
Computational Medicine: Cardiovascular System, Critical Care

Machine Learning in Critical Care

ICU is the most heterogeneous population in the hospital, with the highest rates of acute and chronic multimorbidity. In this project (with A Strub, J Malycha, O Redfern, P Watkinson) we apply machine learning clustering algorithms to identify subgroups of ICU patients having similar characteristics. We show how to use global sensitivity analysis to test the stability of the resulting clustering algorithms.

In this project I lead the effort (with H Khandahari, J Bedford, A Johnson (MIT), P Watkinson, L Tarassenko and much appreciated help form Dani Kiyasseh) to apply Active Learning techniques to ECG labelling for arrhythmia classification in the ICU. We aim to re-label all the MIMIC waveforms to produce one of the largest dataset of its kind.

Sepsis is a life-threatening condition occurring in an estimated 30 million people worldwide and with 6 million people die from sepsis each year. Here (with J Hawrych and J Morrill) we develop a machine learning algorithm that predicts the occurrence of sepsis in the ICU using routinely collected data: vital signs, laboratory values and demographics. This is a follow-up work on the 2019 Physionet Challenge (with MAF Pimentel, A Mahdi, O Redfern, MD Santos, L Tarassenko), Uncertainty-Aware Model for Reliable Prediction of Sepsis in the ICU. Computing in Cardiology (2019), 1-4. [Pdf]

F Andreotti, O Carr, MAF Pimentel, A Mahdi, M De Vos
Comparing feature-based classifiers and Convolutional Neural Networks to detect arrhythmia from short segments of ECG. Computing in Cardiology 44 (2017), 1-4. [Pdf]

Mathematical Modelling in Cardiovascular Disease

A Mahdi, LC Armitage, L Tarassenko, P Watkinson
Estimated prevalence of hypertension and undiagnosed hypertension in a large inpatient population: A cross-sectional observational study. In preparation (2020)

A Mahdi, P Watkinson, RJ McManus, L Tarassenko
Circadian blood pressure variations computed from 1.7 million measurements in an acute hospital setting.
American Journal of Hypertension 32 (2019), 1154-1161 [Pdf]

A Mahdi, GD Clifford, SJ Payne
A model for generating synthetic blood pressure waveform

G Mader, MS Olufsen, A Mahdi
Modeling cerebral blood flow velocity during orthostatic stress