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Statistical Learning and Adaptive Observation in Clinical Prediction: Methodology and Applications


Project Description

Clinical prediction models (CPMs), which predict the occurrence of an event of interest given what is known about an individual, could enable a personalised and preventative approach to healthcare. However, existing CPMs are usually based on cross-sectional data, thereby ignoring the rich longitudinal information in medical records. As such, exploring ways of incorporating longitudinal data into CPMs is an active area of research.

One problem is that the location/setting of where longitudinal observations are recorded (e.g. primary care or secondary care) can influence the recorded observations, which can introduce bias into the CPMs if not properly accounted for. Additionally, limited resources restrict how frequently healthcare professionals can observe longitudinal information about patients; in principle, CPMs could be used to triage adaptive periods of high- and low-frequency observation for patients, but methodological research is required before this can be done in practice.

Therefore, this PhD project will develop statistical methods to allow the derivation of CPMs that “learn” from patient-level behaviours and observation patterns. This PhD has initially two avenues of investigation. Firstly, we will explore if knowing how and where a risk factor has been measured leads to improved prediction of clinical outcomes. Secondly, we will look to develop probabilistic modelling frameworks to evaluate whether there are heterogeneous groups of patients who need stratified/personalised interventions.

Objectives of this PhD include:
1. Use routinely collected healthcare data to motivate and apply methodological development.
2. Investigate how to exploit information potentially contained within the setting of data collection.
3. Develop probabilistic modelling frameworks to evaluate whether there are heterogeneous groups of patients that require personalised interventions.

This PhD is funded by EPSRC and Microsoft Research. Dr Danielle Belgrave, Microsoft Research, will jointly supervise this project, and it is anticipated that the student will visit Microsoft Research Cambridge during the project.

Entry Requirements
We would seek to recruit a student with significant experience in mathematics, statistics and/or computer science. Applicants are expected to hold, or about to obtain, a minimum upper second class (ideally first-class) undergraduate degree (or equivalent) in a relevant discipline, preferably enhanced with a relevant MSc. While advantageous, experience with health/medical data would not be required, as this would be addressed during the PhD.

Funding Notes

EPSRC iCASE Award with Microsoft Research. Scholarship funding is for a duration of four years commencing in September 2019 and covers UK/EU tuition fees, an annual stipend of £18000, travel to conferences and computing equipment. Provision for research visits at Microsoft Research Cambridge will also be covered. On the online application form select PhD Health Informatics .

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Martin, G.P., Mamas, M.A., Peek, N., Buchan, I., Sperrin, M., A Multiple-Model Generalisation of Updating Clinical Prediction Models. Stats in Medicine. (2017). DOI: 10.1002/sim.7586

Martin G.P., Mamas M.A., Peek N., Buchan I., Sperrin M. Clinical Prediction in Defined Populations: a simulation study investigating when and how to aggregate existing models. BMC Medical Research Methodology. 2017 Jan 6;17(1):1. Available from, DOI: 10.1186/s12874-016-0277-1

Sperrin M., Petherick E., Badrick E. Informative Observation in Health Data: Association of Past Level and Trend with Time to Next Measurement. Studies in health technology and informatics. 2017;261-265. Available from, DOI: 10.3233/978-1-61499-753-5-261

Belgrave, D., Granell, R., Simpson, A., Guiver, J., Bishop, C., Buchan, I., Henderson, A.J., and Custovic, A. "Developmental profiles of eczema, wheeze, and rhinitis: two population-based birth cohort studies." PLoS medicine 11, no. 10 (2014): e1001748.

Belgrave, D., Buchan, I., Bishop, C., Lowe, L., Simpson, A., and Custovic, A. "Trajectories of lung function during childhood." American journal of respiratory and critical care medicine 189, no. 9 (2014): 1101-1109.

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