Computing and Information Systems - Research Publications
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ItemIdentification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning(SCITEPRESS - Science and Technology Publications, 2024)Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial.
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Iteme-Enabling international cancer research: lessons being learnt in the ENS@T-CANCER Project(IEEE Computer Society Press, 2013)
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ItemA collaborative infrastructure supporting international adrenal cancer research( 2012)A wealth of information about adrenal cancer exists in many individual specialist centres around the world. The cancers themselves are very rare, often fatal and no common consensus on optimal treatment strategies exists, and certainly no treatments targeted to the individual genetic makeup of the tumours and individuals. In order to conduct effective and progressive research into these tumours and the surrounding conditions and treatments of individuals, it is essential to pool the expertise from specialist centres that exist in each country. The ENSAT-CANCER project is a 5-year European Union FP7-funded project tasked with this, through the development of an online environment that holds core data from a body of patients aligned with identified needs from leading specialists in the field. These data sets are also augmented with a host of tools and features that enable and support the research in this domain. This presentation will describe some of the novel features that have been developed in the project including biobank labelling and “match-making” services between centres. The presentation will also cover the hurdles involved in putting together such an enterprise – such as ethical approval for international data sharing and the establishment of canonical identification systems, and how these have been successfully overcome. The ENSAT-CANCER platform has now been used to support a portfolio of major international genetically targeted clinical trials and outcome studies. The presentation will describe the different processes involved in connecting and effectively sharing data and making best use of the ENSAT-CANCER platform. To date, the registry holds over 2900 patient cases and continues to grow every day.
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ItemA distributed clinical data platform for physiological studies in the brain trauma domain(IEEE Computer Society, 2010)There are many serious and acute physiological conditions about which we have incomplete medical knowledge that can support optimal healthcare intervention. To develop effective treatments a wealth of clinical data is required for collection, analysis and feedback. Such data often does exist but is typically held in a variety of different formats and locations. This paper describes the EU FP7-funded Avert-IT project (www.avert-it.org), which has developed an integrated, real-time physiological data grid infrastructure (HypoNet) to address the specific issue of prediction of hypotensive events in the brain trauma domain and is currently being used as part of a large multi-centre clinical trial. The implementation and application of the HypoNet system is described here.
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ItemTrigger characteristics of EUSIG-defined hypotensive events.(Springer Nature, 2012)BACKGROUND: Hypotension is a recognized -secondary insult after traumatic brain injury (TBI). There are many definitions of hypotension, an often cited example being the Brain Trauma Foundation's current (2007) "Guidelines for the Management of Severe Traumatic Brain Injury," which defines hypotension as systolic pressure <90 mmHg. However, this same document declares "The importance of mean arterial pressure, as opposed to systolic pressure should also be stressed, …." Our work shows that when using the Edinburgh University Secondary Insult Grades (EUSIG) definitions, which require monitoring of both systolic and mean arterial pressures, that most hypotensive events are in fact triggered by a breach of the mean arterial level of 70 mmHg. We suggest that close monitoring of mean arterial pressure would enable clinical teams to avoid more potentially damaging hypotensive events. MATERIALS AND METHODS: An analysis of 100 patients from the Brain-IT database was performed. Using the EUSIG definitions, 2,081 events can be obtained by analyzing the systolic and mean blood pressures on a minute by minute basis. A software program was written to identify and classify the trigger pattern for each event. A categorical analysis of these triggering patterns has been carried out. KEY RESULTS: Our analysis shows that most events are triggered by a drop in mean arterial pressure. In fact a large number of events (91%) occur where the mean arterial pressure is below the threshold limits whereas the systolic pressure does not cross the 90 mmHg limit at all. CONCLUSION: We suggest that more emphasis should be placed on closely monitoring mean arterial pressure as well as systolic pressure when trying to guard against hypotensive problems in traumatically brain injured patients. In future work we will study the underlying physiological mechanisms and attempt to further classify concomitant conditions that may be contributing to the onset of a hypotensive event.
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ItemE-Infrastructures for clinical epidemiological studies across Scotland(International Association for Development of the Information Society (IADIS), 2008)As the proliferation of digital data about individuals increases the opportunities for leveraging this information to benefit society become correspondingly greater. This is especially true in the domain of e-Health where a large number of disparate clinical data resources exist around the world, often housed in individual systems, but with great potential to advance medical and health-care provision if harnessed together and linked with other data resources. In this paper we present a variety of projects that federate such health and other data through re-usable and adaptable e-Infrastructures targeted to the needs of the Scottish and wider e-Research communities. At the heart of all these systems and to counter societies natural wariness of such systems and their use of their personal information are fine grained and adaptable security systems which restrict and enforce access to data to authorised individuals. In this paper we outline these e- Infrastructure architectures, their associated security models and how we are applying them to support epidemiological studies.
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ItemGrid infrastructures for secure access to and use of bioinformatics data: experiences from the BRIDGES project(IEEE Computer Society, 2006)The BRIDGES project was funded by the UK Department of Trade and Industry (DTI) to address the needs of cardiovascular research scientists investigating the genetic causes of hypertension as part of the Wellcome Trust funded (£4.34M) cardiovascular functional genomics (CFG) project. Security was at the heart of the BRIDGES project and an advanced data and compute grid infrastructure incorporating latest grid authorisation technologies was developed and delivered to the scientists. We outline these grid infrastructures and describe the perceived security requirements at the project start including data classifications and how these evolved throughout the lifetime of the project. The uptake and adoption of the project results are also presented along with the challenges that must be overcome to support the secure exchange of life science data sets. We also present how we will use the BRIDGES experiences in future projects at the National e-Science Centre.
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ItemTechnical challenges in leveraging distributed clinical data(IASTED, 2008-12-01)
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ItemTowards decentralised security policies for e-health collaborations(IEEE, 2008-11-17)