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Life-time trajectories of patients with multimorbidity: generative deep learning models based on electronic health records


Roberto Ayala Solares 

Kazem Rahimi


Partly because of our advances in medical research, there has been a rapid rise in the number of patients who suffer multiple chronic conditions. However, most research to date has focused on single diseases. How diseases cluster, how diagnoses and their management interact, and how prognosis is affected by such patterns are not well understood. Such complexities are not unique to medicine and healthcare. Application of deep learning has provided some promising results in tackling some of these issues elsewhere but evidence for their value in big health data is limited.  A DPhil student is sought to join the Oxford Martin programme on Deep Medicine to explore patterns and trajectories in patients with multiple chronic health problems with the aid of generative deep learning models (deep auto-encoders, recurrent neural networks, LSTM models). Such models will learn the key underlying processes that generate the patient journeys (and hence the name generative), and thus, can project from partial (i.e., past) data to future data. This is general predictive power, i.e., a model that can predict anything in future.

We aim to explore such models for the prediction of critical events such as onset of key diseases (diabetes, dementia, heart failure and stroke, to name a few), as well as phenomapping the diseases and clustering of patients.


This project would be suitable for a candidate with 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. Experience working with deep learning frameworks (TensorFlow, PyTorch, Keras) would be a strong advantage. 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.