Research




NERC - Probability, Uncertainty & Risk in the Environment

 PURE: knowledge exchange network

 Collaborative Oxford Network for Bipolar Research to Improve Outcomes

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Remote health diagnosis and monitoring in the time of COVID-19

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Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms.  The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotel
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Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans

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  Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed.  Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%.  Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels
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A racing heart for St Valentine's day

Happy St Valentine's Day A racing heart might be a sign of love or fear ... Using a mathematical model of the electrocardiogram produced by a human heart, we speed up the heart to produce some interesting music. Synthetic ECG generator: ECGSYN --> McSharry PE, Clifford GD, Tarassenko L, Smith L. (2003). A dynamical model for generating synthetic electrocardiogram signals . IEEE Transactions on Biomedical Engineering 50(3): 289-294; March 2003.
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