Announcement from Dr Seref Arikan and Prof David Ingram
We are pleased, as candidate and principal supervisor, to be able to announce publication on the UCL research repository of Seref Arikan’s PhD thesis.
An experimental study and evaluation of a new architecture for clinical decision support - integrating the openEHR specifications for the Electronic Health Record with Bayesian Networks
Healthcare informatics still lacks wide-scale adoption of intelligent decision support methods, despite continuous increases in computing power and methodological advances in scalable computation and machine learning, over recent decades. The potential has long been recognised, as evidenced in the literature of the domain, which is extensively reviewed.
The thesis identifies and explores key barriers to adoption of clinical decision support, through computational experiments encompassing a number of technical platforms. Building on previous research, it implements and tests a novel platform architecture capable of processing and reasoning with clinical data. The key components of this platform are the now widely implemented openEHR electronic health record specifications and Bayesian Belief Networks.
Substantial software implementations are used to explore the integration of these components, guided and supplemented by input from clinician experts and using clinical data models derived in hospital settings at Moorfields Eye Hospital. Data quality and quantity issues are highlighted. Insights thus gained are used to design and build a novel graph-based representation and processing model for the clinical data, based on the openEHR specifications. The approach can be implemented using diverse modern database and platform technologies.
Computational experiments with the platform, using data from two clinical domains – a preliminary study with published thyroid metabolism data and a substantial study of cataract surgery – explore fundamental barriers that must be overcome in intelligent healthcare systems developments for clinical settings. These have often been neglected, or misunderstood as implementation procedures of secondary importance. The results confirm that the methods developed have the potential to overcome a number of these barriers.
The findings lead to proposals for improvements to the openEHR specifications, in the context of machine learning applications, and in particular for integrating them with Bayesian Networks. The thesis concludes with a roadmap for future research, building on progress and findings to date.”
The wide methodological and experimental scope of the research reported in the thesis embraces:
The thesis was examined by:
External Examiners: Prof Jim Davies, Professor of Computer Science, University of Oxford;
Prof Martin Severs, Professor of Health Care for Older People, University of Portsmouth,
and NHSDigital Medical Director and Caldicott Guardian
UCL Internal Examiner: Prof Philip Treleaven, Professor of Computer Science, University College London
We hope that the findings of the thesis will make a significant future contribution to the optimisation of openEHR persistence implementations within open data platforms, as they are called upon and extended to tackle clinical and population health informatics challenges of ever-increasing scale and analytical complexity.
Dr Seref Arikan
Centre for Health Informatics and Multiprofessional Education at UCL; Ocean Informatics UK
Prof David Ingram
Emeritus Professor of Health Informatics, UCL; President and Chair of the Board of Governors, openEHR Foundation;
Trustee of the OpenEyes Charity;
Academic Board of the Planetearth Institute
October 23rd 2017