Closing date : 21/08/2016
Reference : BM&H-08580
Faculty / Organisational unit : Biology, Medicine & Health
School / Directorate : School of Health Sciences
Division : Division of Population Health, Health Services Research and
Primary Care
Employment type : Fixed Term
Duration : As soon as possible until 30th April 2018
Location : Oxford Road, Manchester
Salary : £30,738 to £37,768 per annum
Hours per week : Full Time
Further information/to apply:
https://www.jobs.manchester.ac.uk/displayjob.aspx?jobid=11899

The Centre for Health Informatics is seeking a research associate in
health data science to drive innovation at the interface between
biostatistics, machine learning, and software engineering. Working
with clinical, IT and healthcare data experts you will drive the
modelling of health data using state-of-the-art methods from
biostatistics and machine learning.

Ideally, you will have previous relevant experience in academia or
industry encompassing a strong mathematical element and significant
statistical knowledge.  Ideally, you will be educated to PhD level in
Statistics, Machine Learning, or related fields; have experience in
the analysis of complex healthcare problems and datasets; and have
significant statistical modelling and programming experience.  If you
do not have a PhD you will be considered if you have significant
demonstrable research and/or industry experience and are in possession
of a relevant higher degree. Domain knowledge of statistical and
machine learning analyses of health, biological or social datasets;
epidemiological models and complex causal inference; and multi-level
regression are highly sought.

Safe Prescribing of Medication

Certain medications may be contra-indicated in given populations, or
may require high levels of patient monitoring when used. Examples of
such hazards: prescribing beta blockers to patients with asthma, or
failure to test thyroid function regularly in patients receiving
amiodarone. Using linked primary and secondary data, we have developed
a tool to identify such medication safety hazards. We would like to
evaluate the potential impact of such hazards: i.e. if the rate of
such hazards were reduced, how many medication-related hospital
admissions could be prevented? We are also developing and evaluating
interventions (such as dashboards) aiming to reduce the prevalence of
these hazards.

Dynamic and Longitudinal Approaches to Predictive Modelling

Clinical predictive models are used across healthcare to aid in
decision making, planning and audit; these are often based on simple
logistic regression models using information about a patient at a
fixed point in time. We are developing methodology to exploit the
richer sources of data that are now available to us. For example:

Utilising longitudinal biomarker and risk factor information: can a
model be improved by using the full history of risk factor changes
over time?
Responding to emerging data in an on-line fashion: as health data
becomes more ‘connected’ can our models respond dynamically to
emerging trends in outcome rates, policy changes or secular trends?
Multiple outcomes and comorbidities: can we build joint, integrated
models that consider multiple outcomes simultaneously (e.g. stroke,
heart attack, onset of diabetes)?

The School of Health Sciences is committed to promoting equality and
diversity, including the Athena SWAN charter for promoting women’s
careers in STEMM subjects (science, technology, engineering,
mathematics and medicine) in higher education. The [School/Institute]
received a Bronze Award in 2013 for their commitment to the
representation of women in the workplace and we particularly welcome
applications from women for this post. Appointment will always be made
on merit. For further information, please visit
http://www.mhs.manchester.ac.uk/about-us/athena/.

Please note that we are unable to respond to enquiries, accept CV's or
applications from Recruitment Agencies

Enquiries about the vacancy, shortlisting and interviews:

Matthew Sperrin, Lecturer in Health Data Science

Email: [log in to unmask]

Tel: 0161 306 7629

General enquiries:

Email: [log in to unmask]

Tel: 0161 275 4499

Technical support:

Email: [log in to unmask]

Tel: 01565 818 234


Further information and to apply:
https://www.jobs.manchester.ac.uk/displayjob.aspx?jobid=11899

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