Sara joined NDORMS in 2016, as the lead data analyst for the Health Services Delivery group and the Musculoskeletal Pharmaco-epidemiology group. She was previously based at the Institute of Biomedical Engineering (IBME) as a postdoctoral researcher in the Biomedical Image Analysis Lab, and in the Biomedical Signal Processing and Computational Health Informatics Lab.
Sara completed her DPhil in Engineering Science at the IBME, as a Rhodes Scholar. She previously received the MSc degree in Biomedical Engineering from the University of Oxford in 2009, as a Qualcomm Scholar, following an undergraduate degree in Electronics Engineering from the National University of Sciences and Technology, Karachi.
Her research interests include signal processing and machine learning, with applications in health informatics such as patient monitoring and telehealth.
Sara's thesis explored Bayesian parametric techniques for providing early warning of patient deterioration, using time-series physiological data, and developed methods for multi-class classification of patient abnormalities using vital-sign data acquired from a large study with collaborators in the University of Pittsburgh Medical Centre. Sara led the data collection and statistical analysis of the multi-phase Cancer Hospital Study undertaken in the Cancer Hospital in Oxford, UK.
Sara teaches the course Statistical Learning in R in collaboration with The Oxford e-Research Centre Advanced Research Computing and the Computer Science Department. Sara is currently supervising a DPhil student and an MSc student, and she is interested in supervision opportunities.
Martín-Merino E. et al, (2017), Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
REDUCTION IN FRATURERATES WITH DENOSUMAB COMPARED TO ALENDRONATE IN TREATMENT NAIVE PATIENTS: A PROPENSITY-MATCHED 'REAL WORLD' COHORT AND INSTRUMENTAL VARIABLE ANALYSIS
Khalid S. et al, (2017), OSTEOPOROSIS INTERNATIONAL, 28, S64 - S66
A CUT-OFF POINT IN THE OXFORD KNEE SCORE TO IDENTIFY PATIENTS WITH CHRONIC PAIN AFTER KNEE REPLACEMENT
Pinedo-Villanueva R. et al, (2017), OSTEOPOROSIS INTERNATIONAL, 28, S274 - S274
CHARACTERISING ANTI-OSTEOPOROSIS DRUG USERS IN REAL WORLD PRIMARY CARE SETTINGS IN SPAIN: A DATA-DRIVEN CLUSTER ANALYSIS
Khalid S. et al, (2017), OSTEOPOROSIS INTERNATIONAL, 28, S431 - S432
An oxford knee score cut-off point to identify patients with chronic pain after knee replacement for a complex intervention trial
Khalid S. et al, (2017), TRIALS, 18