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Machine learning methods for enhancing the quality of big data for medical research


Amir Payberah

Dexter Canoy

Kazem Rahimi


The growing access to a wide range of big data for medical research has enabled investigation of questions of high relevance to medical care and policy. However, a common problem for researchers using large-scale research or routine datasets is the data quality issue (e.g., data missing-ness, measurement errors, variability of and non-commensurate measurements between different datasets). Although several statistical approaches have been proposed to enhance the quality of data for research, they tend to be based on linear models with limited consideration of important interactions. Furthermore, conventional methods do not scale well as the data gets bigger and when the number of variables is large. A promising approach to overcome these limitations is to use machine learning/deep learning methods. There are a few studies that show how these techniques can advance traditional methods in term of higher accuracy and better scalability. However, applying such techniques on massive data is not yet well investigated, partly due to the lack of support for parallelisation in existing solutions. Therefore, it's necessary to build an efficient and scalable model that ensures an accuracy comparable to the centralised models.

A DPhil student is sought to join the Oxford Martin programme on Deep Medicine to apply and develop deep learning methods that aim to test the accuracy and efficiency of several modeling approaches, and to compare them with existing methods.


This project would be suitable for a candidate with a strong quantitative background (e.g. mathematics, informatics or statistics) and interest in applied research methods that are likely to have a major impact on population health. This project will be part of a new interdisciplinary programme entitled ‘Deep Medicine’ at the George Institute for Global Health. The research team provides expert individual supervision and support from several of experienced and enthusiastic researchers with backgrounds in clinical medicine, statistics, epidemiology, computer science and informatics. Further support in grant writing, high-impact scientific publications and career development will be provided.

As well as the specific training detailed above, students will have access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.