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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ItemRun or Pat: Using Deep Learning to Classify the Species Type and Emotion of Pets(EEE, 2021-01-01)Deep learning has been applied in many contexts. In this paper we present a novel application area: to detect the species type and emotion of pets with focus on a diverse set of dog and cat collections comprising 52 dog and 23 cat species. Building on an extensive collection of labelled images with over 300 images per species type, we explore a range of deep learning models to develop a classifier for species type and their associated emotion. We outline the realization of the technical solution delivered through a mobile application (iPhone/Android) and present results based on feedback based on real world adoption and utilisation by the broader mobile application community.
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ItemEliciting group judgements about replicability: A technical implementation of the IDEA Protocol(Hawaii International Conference on System Sciences, 2021-01-01)In recent years there has been increased interest in replicating prior research. One of the biggest challenges to assessing replicability is the cost in resources and time that it takes to repeat studies. Thus there is an impetus to develop rapid elicitation protocols that can, in a practical manner, estimate the likelihood that research findings will successfully replicate. We employ a novel implementation of the IDEA ('Investigate', 'Discuss', 'Estimate' and 'Aggregate) protocol, realised through the repliCATS platform. The repliCATS platform is designed to scalably elicit expert opinion about replicability of social and behavioural science research. The IDEA protocol provides a structured methodology for eliciting judgements and reasoning from groups. This paper describes the repliCATS platform as a multi-user cloud-based software platform featuring (1) a technical implementation of the IDEA protocol for eliciting expert opinion on research replicability, (2) capture of consent and demographic data, (3) on-line training on replication concepts, and (4) exporting of completed judgements. The platform has, to date, evaluated 3432 social and behavioural science research claims from 637 participants.