Clinical data science: Developing a mortality prediction tool
PhDs and postgraduate research
Self-funded PhD students only
School of Computing
Applications accepted all year round
The work on this project could involve:
- data science analysing anonymised clinical data from hospital patients
- contributing to a study furthering medical knowledge or hospital efficiency
The Centre for Healthcare Modelling and Informatics (CHMI) is a long-established health informatics research and innovation group. In collaboration with Portsmouth Hospitals and others, our work in clinical outcome modelling has supported the development of the VitalPAC vital signs collection system and the National Early Warning Score (NEWS) recommended by the Royal College of Physicians and mandated by the NHS, among many other projects.
The aim of the project is to develop a new mortality prediction tool that can be used to identify changes in a hospital’s clinical outcomes and to compare the performance of hospitals. A hospital where significantly more patients die than expected could be underperforming. The expected mortality is based on the number of patients and the seriousness of their condition (obviously, very sick people are more likely to die than those who are not). Current tools used for this (e.g. HSMR and SHMI) are based on the administrative data that records the diagnoses associated with a patient's hospital stay. They adjust for various factors such as the patient's age, sex and their existing medical problems, however, the data can be "gamed" and the quality of the data is variable – for example, it is often recorded long after the patient has left the hospital and only analysed much later.
This project would start from the position that clinical performance should be measured by clinical data. Actual data, recorded as part of the normal delivery of care (such as the results of laboratory tests on blood and vital signs taken at the start of a patient's stay) are less prone to error or gaming, and should provide an objective (physiological) ruler with which to measure a patient's degree of sickness. Several aspects of the problem are open to investigation including how the tool could be calibrated initially and adjusted according to changing practice.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2020/2021 entry (for October 2020 and February 2021 entries)
Home/EU/CI full-time students: £4,407 p/a
Home/EU/CI part-time students: £2,204 p/a
International full-time students: £16,400 p/a
International part-time students: £8,200 p/a
PhD by Publication
External candidates £4,407 p/a
Members of staff £1,680 p/a*
2021/2022 entry (for October 2021 and February 2022 entries)
PhD and MPhil
Home/EU/CI full-time students: £4,407 p/a*
Home/EU/CI part-time students: £2,204 p/a*
International full-time students: £17,600 p/a
International part-time students: £8,800 p/a
All fees are subject to annual increase.
PhD by Publication
External Candidates £4,407 p/a*
Members of Staff £1,720 p/a*
If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent
professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
High level of numeracy and confidence in dealing with data analysis
How to apply
We’d encourage you to contact Prof Briggs (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this PhD opportunity you must quote project code COMP5000220 when applying.