Fatima Batool

I am interested in a range of methodological developments spanning statistics, causal inference, epidemiology, and the analysis of large-scale medical datasets, including both routinely collected information and data from large observational consortia.

The Oxford-RCGP Research and Surveillance Centre (RSC) national sentinel surveillance network, comprising more than 2000 practices, routinely collects electronic health records data. This data is hosted through a trusted research environment (TRE) called Oxford-RCGP Clinical Informatics Digital Hub (ORCHID). I develop statistical and machine learning methods using this data for infectious disease surveillance. I am member of the Clinical Informatics and Health Outcomes Research group (CIHORG) which is led by Professor Simon de Lusignan. I am involved in:

Wellcome 50-year project: Aimed at creating a longitudinal, linked sentinel database covering the fifty years (QQG) of clinical and virology data, with a prospective research platform for the broader research community. I have developed the Wellcome Surveillance Dashboard as part of this initiative.

I am also working on the ObservatARI study, which is funded by Moderna, for the surveillance of ARI-related pathogens such as COVID, influenza, and RSV.

Methodology-wise, my research interest lies in dimensionality reduction, unsupervised machine learning, and causality. This also involves the use of genomics to understand the genetic basis and causal pathways of complex diseases. Disease-wise, I have worked with conditions such as diabetes, strokes, cancer, cardiovascular disease, and neuro-psychiatric disorders.

I have worked with genome-wide association studies (GWAS), eQTL gene expressions across tissues (GTEx Consortium) of EMBL-EBI, GIANT, MEGASTROKE, and DIAGRAM. I have explored multiple cis and trans gene regions, such as GLP1 for risk of cancer and FTO for risk of diabetes, to infer causal pathways relevant to drug discovery. Previously, I have also developed machine learning predictive models for the ageing population. Before that I have developed theoretical Randomized Response Methods (RRMs) for sampling in sensitive surveys.

TEACHING

Most recently, I have lectured and was module organiser for the Programming in C++ for Finance course. Prior to this, I have delivered various sessions in statistics, data science, and machine learning courses to undergraduate, graduate, and industry professionals.

SUPERVISIONS

I am currently supervising an FHS project. I have supervised MPhil, MSc, and undergraduate students and theses.

I am open to research collaborations and advising MSc and PhD students.