Webpage of Dr Adam Mahdi

Felix Krones, DPhil Student, Oxford Internet Institute, University of Oxford
Djavan De Clercq, DPhil Student, Oxford Internet Institute, University of Oxford
Benjamin Duvieusart, MEng Student, Engineering Science, University of Oxford
Nathan Bailey, MSc Student, Oxford Internet Institute, University of Oxford
Alexander Mann, MSc Student, Oxford Internet Institute, University of Oxford
Jian Yeo, MSc Student, Oxford Internet Institute, University of Oxford
Yuxuan Zhang, MSc Student, Oxford Internet Institute, University of Oxford

Declant Grant

MEng thesis: "Machine Learning Approach to Classification of Cardiopulmonary Conditions from Multimodal Data in the ICU"
Department of Engineering Science, University of Oxford, 2021
Award: Qualcomm Prize for best biomedical engineering 4th year project

Paulius Rauba (co-supervised with Bernie Hogan)

MSc thesis: "Armed Conflict and State Policy Preferences: A Statistical and Neural Embedding Approach"
Oxford Internet Institute, University of Oxford, 2021

Hamid Khandahari

MEng thesis: "Active Learning approach to labelling ECG signals for classification of Atrial Fibrillation using the MIMIC III database"
Department of Engineering Science, University of Oxford, 2020

James Hawrych
- MEng thesis: "Path and Deep Signature Approach to Sepsis Prediction in the ICU"
- Department of Engineering Science, University of Oxford, 2020
Frederick G. Denison-Smith

MEng Thesis: "Short-Term Effect of Temperature on Emergency Oxford Hospital Admissions"
Department of Engineering Science, University of Oxford, 2020

Emmanuel Oladokun

MEng thesis: "Statistical Modelling of Circadian Blood Pressure Rhythms in an Acute Hospital Setting"
Department of Engineering Science, University of Oxford, 2020

Akasha Strub

MEng thesis: "Investigating the Use of Clustering Techniques on ICU Patient Data"
Department of Engineering Science, University of Oxford, 2020

From around February 2020 I lead Oxford COVID-19 (OxCOVID19) Project aimed at increasing our understanding of the COVID-19 pandemic and elaborate possible strategies to reduce the impact on the society through the combined power of Statistical, Mathematical Modelling and Machine Learning techniques. [Read more].

ICU is the most heterogeneous population in the hospital, with the highest rates of acute and chronic multimorbidity. In this project 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) 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]

We benchmark a feature-based and a deep learning approach in classifying short ECG segments as proposed by the Physionet/Computing in Cardiology Challenge 2017.

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]

We found that a large number of hospitalised patients in the UK were found to have mean blood pressures that exceeded the diagnostic threshold for hypertension, without any notation of treatment or diagnosis of hypertension on their medical records.

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.

American Journal of Hypertension 34 (2021), 963-972

We found that hospitalised patients’ circadian patterns of BP largely mirror those found in the community. High-quality hospital data may allow for the identification of patients at significant cardiovascular risk through either opportunistic screening or systematic screening.

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]

Dr Adam MAHDI

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Machine Learning in Healthcare