- Judge Group | Health Service Delivery Research Group
- Prieto-Alhambra Group | Musculoskeletal Pharmaco- and Device epidemiology Research Group
- Big Health Data Research Research Group
- ATLAS Research Group
Sara joined the 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.
COMPARATIVE RISK OF VENOUS THROMBOEMBOLISM AMONGST USERS OF DIFFERENT ANTI-OSTEOPOROSIS DRUGS IN THE UK: A PROPENSITY-MATCHED COHORT STUDY USING DATA FROM THE CLINICAL PRACTICE RESEARCH DATALINK
Khalid S. et al, (2016), OSTEOPOROSIS INTERNATIONAL, 27, 673 - 674
COMPARATIVE FRACTURE RISK AMONGST USERS OF DIFFERENT ANTI-OSTEOPOROSIS DRUGS AVAILABLE IN THE UK IN THE NHS: A PROPENSITY-MATCHED COHORT STUDY USING DATA FROM THE CLINICAL PRACTICE RESEARCH DATALINK
Khalid S. et al, (2016), OSTEOPOROSIS INTERNATIONAL, 27, 631 - 631
A Bayesian patient-based model for detecting deterioration in vital signs using manual observations
Khalid S. et al, (2014), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8315, 146 - 158
Khalid S. et al, (2012), IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society, 16, 1231 - 1238
Clifton DA. et al, (2011), IET Seminar Digest, 2011