Contact information
Websites
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Centre for Statistics in Medicine
Research Group
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STAR Programme
STAR - Support and Treatment After Replacement
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ATLAS Programme
ATLAS -Enhanced Recovery for Arthroplasty Patients
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EHDEN
European Health Data & Evidence Network
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OHDSI
Observational Health Data Sciences and Informatics
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National Geographic Society
Early Career Explorer
Sara Khalid
BE, MSc (Oxon), DPhil
University Research Lecturer, Senior Research Fellow in Biomedical Data Science and Health Informatics
- Group Head - Planetary Health Informatics
- Machine Learning Lead - Pharmaco-device Epidemiology Group, Centre for Statistics in Medicine
- National Geographic Explorer - Tracking Plastic Pollution with Remote Monitoring and Machine Learning
- Ambassador for Women in Data Science - University of Oxford
Health Informatics, Intelligent Patient Monitoring, Planetary Health, Real-world Data Science
RESEARCH
Sara leads the Planetary Health Informatics group and the Machine Learning and Big Data Analytics branch of the Musculoskeletal Pharmaco-epidemiology group in NDORMS, which she joined in 2016. She was previously based at the Institute of Biomedical Engineering (IBME) in the Biomedical Image Analysis Lab, the Biomedical Signal Processing Lab, and the Computational Health Informatics Lab. Her research spans health, environment, and conservation. She is a National Geographic Early Career Explorer having secured a grant for using machine learning and remote monitoring for tracking plastic pollution from land to sea.
Sara completed her DPhil in Engineering Science at the IBME, University of Oxford, as a Rhodes Scholar. Prior to that she received a Distinction for her MSc in Biomedical Engineering from the University of Oxford in 2009, as a Qualcomm Scholar. In 2007 she graduated with a BE in Electronics Engineering from the National University of Sciences and Technology in Karachi, Pakistan.
Her research interests include signal processing and machine learning, with applications in health informatics such as patient monitoring, telehealth, and observational research.
Sara's thesis explored Bayesian methods for providing early warning of patient deterioration, using time-series physiological data, and developed machine learning 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.
Teaching and Supervision
Sara teaches data science and machine learning for healthcare research at NDORMS. She is the trainer of the University-wide course "Statistical Learning in R" in collaboration with The Oxford e-Research Centre Advanced Research Computing and the Computer Science Department. She is also a faculty member at the NIHR BRC course "Data analysis: Statistics - designing clinical research and biostatistics", organised by the Musculoskeletal Pharmaco-epidemiology group at NDORMS.
Sara is currently supervising eight DPhil students and an MSc student, as well as visiting PhD students, and she is interested in supervision opportunities.
Recent publications
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Journal article
Khalid S. et al, (2022), Osteoporos int
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2021 American College of Rheumatology / European League Against Rheumatism classification criteria for Microscopic polyangiitis
Journal article
KHALID S. et al, (2022), Arthritis and rheumatology
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2021 American College of Rheumatology / European League Against Rheumatism classification criteria for granulomatosis with polyangiitis
Journal article
KHALID S. et al, (2022), Arthritis and rheumatology
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American College of Rheumatology /European League Against Rheumatism classification criteria for Eosinophilic granulomatosis with polyangiitis
Journal article
KHALID S. et al, (2022), Arthritis and rheumatology
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Unsupervised Learning to Understand Patterns of Comorbidity in 633,330 Patients Diagnosed with Osteoarthritis
Conference paper
Pineda Moncusi M. et al, (2022), 15th international conference on health informatics
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A data integration pipeline towards reliable monitoring of phytoplankton and early detection of harmful algal blooms
Conference paper
KHALID S. et al, (2021)
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Journal article
Khalid S. et al, (2021), Computer methods and programs in biomedicine, 211, 106394 - 106394
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Journal article
Khalid S. et al, (2021), J bone miner res, 36, 2162 - 2176
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Journal article
Xie J. et al, (2021), Jama, 326, 1504 - 1515
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Costes y beneficios del programa de prevención de fractura FLS en España
Conference paper
PINEDO VILLANUEVA R. et al, (2021)