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I have worked on a wide range of topics, ranging from evolution to health genomics. I am particularly interested in how we can use real-world data to tackle cancer and infectious diseases. I would also like to expand my research to include non-communicable diseases such as metabolic disease, cardiovascular disease and diabetes.
Cancer is a highly heterogeneous disease characterised by a myriad of subtypes. Despite advancement in healthcare services and treatment regimens, cancer patients still face significantly higher mortality rates and impairments in their quality of life. Cancer patients often have numerous interactions with healthcare systems, enabling the use of electronic health records for the generation of models on clinical events associated with cancer emergence and progression. Furthermore, the presence of additional disease(s) in an individual may influence the timing of cancer diagnosis, treatment and prognosis, as do demographic and socioeconomic factors.
We are interested in modelling cancer trajectories using electronic health records and multi-omics data to help bring us a step closer to achieving personalised care and to reveal how cancers progress from symptoms to neoplasm to death and how risk factors may influence these trajectories.
Early cancer diagnosis is of paramount importance if we are to improve survival chances and reduce treatment costs. We employ machine learning tools to predict the likelihood of cancer based on disease-specific and disease-agnostic risk factors. Details on how cancer phenotypes are choreographed throughout an individual’s lifespan will have immense translational implications as this information can improve cancer diagnoses, prognosis stratification, treatment recommendation/efficacy and resource utilisation.
I have worked on a wide range of topics, ranging from evolution to health genomics. I am particularly interested in how we can use real-world data to tackle cancer and infectious diseases. I would also like to expand my research to include non-communicable diseases such as metabolic disease, cardiovascular disease and diabetes.
Cancer is a highly heterogeneous disease characterised by a myriad of subtypes. Despite advancement in healthcare services and treatment regimens, cancer patients still face significantly higher mortality rates and impairments in their quality of life. Cancer patients often have numerous interactions with healthcare systems, enabling the use of electronic health records for the generation of models on clinical events associated with cancer emergence and progression. Furthermore, the presence of additional disease(s) in an individual may influence the timing of cancer diagnosis, treatment and prognosis, as do demographic and socioeconomic factors.
We are interested in modelling cancer trajectories using electronic health records and multi-omics data to help bring us a step closer to achieving personalised care and to reveal how cancers progress from symptoms to neoplasm to death and how risk factors may influence these trajectories.
Early cancer diagnosis is of paramount importance if we are to improve survival chances and reduce treatment costs. We employ machine learning tools to predict the likelihood of cancer based on disease-specific and disease-agnostic risk factors. Details on how cancer phenotypes are choreographed throughout an individual’s lifespan will have immense translational implications as this information can improve cancer diagnoses, prognosis stratification, treatment recommendation/efficacy and resource utilisation.
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arxiv(2024)
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International Journal of Obesity (2023)
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The Lancet Digital Healthno. 1 (2023): e16-e27
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Stefanie H. Mueller,Alvina G. Lai,Maria Valkovskaya,Kyriaki Michailidou,Manjeet K. Bolla,Qin Wang,Joe Dennis,Michael Lush, Zomoruda Abu-Ful,Thomas U. Ahearn,Irene L. Andrulis,Hoda Anton‐Culver,
CLINICAL AND TRANSLATIONAL MEDICINEno. 6 (2022): e897-e897
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